Yelp created the hotdog app from Silicon Valley (only better)   

Yelp’s Photos Team is developing AI and machine learning algorithms to understand content in user photos. Using neural networks, the systems are able to identify specific attributes that make a photo “beautiful.” The neural network can also identify and categorize based on the type of dish in the photo ー meaning that Silicon Valley’s hotdog-identifying app actually exists. The motivation behind the AI is the rise of photo sharing at restaurants. People love snapping and gramming the trendiest spots, dishes, and menus, but they also love posting on Yelp. Over 100,000 photos are uploaded to Yelp per day, so the…

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Or just read more coverage about: Yelp
          Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods   
In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumor hypoxia, but a standardized measurement is currently unavailable in clinical practice. Different types of manual measurements have been proposed in clinical research, but in this paper we focus on a recently published approach that quantifies the number and proportion of hypoxic regions using high resolution (immuno-)fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen of a tumor. We introduce new machine learning-based methodologies to automate this measurement, where the main challenge is the fact that the clinical annotations available for training the proposed methodologies consist of the total number of normoxic, chronically hypoxic, and acutely hypoxic regions without any indication of their location in the image. Therefore, this represents a weakly-supervised structured output classification problem, where training is based on a high-order loss function formed by the norm of the difference between the manual and estimated annotations mentioned above. We propose four methodologies to solve this problem: 1) a naive method that uses a majority classifier applied on the nodes of a fixed grid placed over the input images; 2) a baseline method based on a structured output learning formulation that relies on a fixed grid placed over the input images; 3) an extension to this baseline based on a latent structured output learning formulation that uses a graph that is flexible in terms of the amount and positions of nodes; and 4) a pixel-wise labeling based on a fully-convolutional neural network. Using a data set of 89 weakly annotated pairs of IF and HE images from eight tumors, we show that the quantitativ- results of methods (3) and (4) above are equally competitive and superior to the naive (1) and baseline (2) methods. All proposed methodologies show high correlation values with respect to the clinical annotations.
          Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction   
Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the locations of neuronal voxels using image segmentation methods, prior to applying tracing or reconstruction techniques. This preprocessing step is expected to remove noises in the data, thereby leading to improved reconstruction results. In this paper, we proposed to use 3-D convolutional neural networks (CNNs) for segmenting the neuronal microscopy images. Specifically, we designed a novel CNN architecture, that takes volumetric images as the inputs and their voxel-wise segmentation maps as the outputs. The developed architecture allows us to train and predict using large microscopy images in an end-to-end manner. We evaluated the performance of our model on a variety of challenging 3-D microscopy images from different organisms. Results showed that the proposed methods improved the tracing performance significantly when combined with different reconstruction algorithms.
          Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection   
Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human–robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.
          Method elucidates inner workings of neural networks   
A new technique helps elucidate the inner workings of neural networks trained on visual data.
          New technique elucidates the inner workings of neural networks trained on visual data   
Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for today's best-performing artificial intelligence systems, from speech recognition systems, to automatic translators, to self-driving cars.
          Method elucidates inner workings of neural networks   
(Massachusetts Institute of Technology) A new technique helps elucidate the inner workings of neural networks trained on visual data.
          ARM SoCs Take Soft Roads to Neural Nets   
NXP, Qualcomm, and ARM have all released math libraries to run neural network inference jobs on their existing CPU, GPU, and DSP cores.
          I love you for the day   

'I love you for the day' is an installation created by Matthias Maurer & Guillaume Massol situated at the unlikely intersection between mass surveillance and poetry.

The installation is constantly looking around, focusing from time to time on passers-by. Once focused on a person it’s tracking his/her face and breaks it down into fragments, generating a constantly evolving mosaic of eyes, mouthes and noses. As it watches, it generates lyrics and music based on facial features. While on idle mode it reload bits of previously tracked faces and keeps on generating soundscapes and lyrics.

Text and music are created by a multi-layer Recurrent Neural Network. A model trained on love songs generates the text of the installation character by character, while another model – trained on heavy metal – generates the melody which is slowed down and played with electronic instruments.

The soundtrack of the video as well as the name of the installation has been generated by a RNN as well.

Cast: Guillaume

Tags: LED, machine learning, generative, installation, ai and generative music


          Artificial Synapses Could Lead to Smarter AI   
By replicating the function of the human brain's 100 trillion synapses, scientists hope to boost the versatility of artificial neural networks.
          大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理   

大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

作者:Stephen Cui

一、大数据分析在商业上的应用

1、体育赛事预测

世界杯期间,谷歌、百度、微软和高盛等公司都推出了比赛结果预测平台。百度预测结果最为亮眼,预测全程64场比赛,准确率为67%,进入淘汰赛后准确率为94%。现在互联网公司取代章鱼保罗试水赛事预测也意味着未来的体育赛事会被大数据预测所掌控。

“在百度对世界杯的预测中,我们一共考虑了团队实力、主场优势、最近表现、世界杯整体表现和博彩公司的赔率等五个因素,这些数据的来源基本都是互联网,随后我们再利用一个由搜索专家设计的机器学习模型来对这些数据进行汇总和分析,进而做出预测结果。”—百度北京大数据实验室的负责人张桐


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

2、股票市场预测

去年英国华威商学院和美国波士顿大学物理系的研究发现,用户通过谷歌搜索的金融关键词或许可以金融市场的走向,相应的投资战略收益高达326%。此前则有专家尝试通过Twitter博文情绪来预测股市波动。

理论上来讲股市预测更加适合美国。中国股票市场无法做到双向盈利,只有股票涨才能盈利,这会吸引一些游资利用信息不对称等情况人为改变股票市场规律,因此中国股市没有相对稳定的规律则很难被预测,且一些对结果产生决定性影响的变量数据根本无法被监控。

目前,美国已经有许多对冲基金采用大数据技术进行投资,并且收获甚丰。中国的中证广发百度百发100指数基金(下称百发100),上线四个多月以来已上涨68%。

和传统量化投资类似,大数据投资也是依靠模型,但模型里的数据变量几何倍地增加了,在原有的金融结构化数据基础上,增加了社交言论、地理信息、卫星监测等非结构化数据,并且将这些非结构化数据进行量化,从而让模型可以吸收。

由于大数据模型对成本要求极高,业内人士认为,大数据将成为共享平台化的服务,数据和技术相当于食材和锅,基金经理和分析师可以通过平台制作自己的策略。

http://v.youku.com/v_show/id_XMzU0ODIxNjg0.html

3、市场物价预测

CPI表征已经发生的物价浮动情况,但统计局数据并不权威。但大数据则可能帮助人们了解未来物价走向,提前预知通货膨胀或经济危机。最典型的案例莫过于马云通过阿里B2B大数据提前知晓亚洲金融危机,当然这是阿里数据团队的功劳。

4、用户行为预测

基于用户搜索行为、浏览行为、评论历史和个人资料等数据,互联网业务可以洞察消费者的整体需求,进而进行针对性的产品生产、改进和营销。《纸牌屋》选择演员和剧情、百度基于用户喜好进行精准广告营销、阿里根据天猫用户特征包下生产线定制产品、亚马逊预测用户点击行为提前发货均是受益于互联网用户行为预测。

购买前的行为信息,可以深度地反映出潜在客户的购买心理和购买意向:例如,客户 A 连续浏览了 5 款电视机,其中 4 款来自国内品牌 S,1 款来自国外品牌 T;4 款为 LED 技术,1 款为 LCD 技术;5 款的价格分别为 4599 元、5199 元、5499 元、5999 元、7999 元;这些行为某种程度上反映了客户 A 对品牌认可度及倾向性,如偏向国产品牌、中等价位的 LED 电视。而客户 B 连续浏览了 6 款电视机,其中 2 款是国外品牌 T,2 款是另一国外品牌 V,2 款是国产品牌 S;4 款为 LED 技术,2 款为 LCD 技术;6 款的价格分别为 5999 元、7999 元、8300 元、9200 元、9999 元、11050 元;类似地,这些行为某种程度上反映了客户 B 对品牌认可度及倾向性,如偏向进口品牌、高价位的 LED 电视等。

http://36kr.com/p/205901.html

5、人体健康预测

中医可以通过望闻问切手段发现一些人体内隐藏的慢性病,甚至看体质便可知晓一个人将来可能会出现什么症状。人体体征变化有一定规律,而慢性病发生前人体已经会有一些持续性异常。理论上来说,如果大数据掌握了这样的异常情况,便可以进行慢性病预测。

6、疾病疫情预测

基于人们的搜索情况、购物行为预测大面积疫情爆发的可能性,最经典的“流感预测”便属于此类。如果来自某个区域的“流感”、“板蓝根”搜索需求越来越多,自然可以推测该处有流感趋势。

Google成功预测冬季流感:
2009年,Google通过分析5000万条美国人最频繁检索的词汇,将之和美国疾病中心在2003年到2008年间季节性流感传播时期的数据进行比较,并建立一个特定的数学模型。最终google成功预测了2009冬季流感的传播甚至可以具体到特定的地区和州。

7、灾害灾难预测

气象预测是最典型的灾难灾害预测。地震、洪涝、高温、暴雨这些自然灾害如果可以利用大数据能力进行更加提前的预测和告知便有助于减灾防灾救灾赈灾。与过往不同的是,过去的数据收集方式存在着死角、成本高等问题,物联网时代可以借助廉价的传感器摄像头和无线通信网络,进行实时的数据监控收集,再利用大数据预测分析,做到更精准的自然灾害预测。

8、环境变迁预测

除了进行短时间微观的天气、灾害预测之外,还可以进行更加长期和宏观的环境和生态变迁预测。森林和农田面积缩小、野生动物植物濒危、海岸线上升,温室效应这些问题是地球面临的“慢性问题“。如果人类知道越多地球生态系统以及天气形态变化数据,就越容易模型化未来环境的变迁,进而阻止不好的转变发生。而大数据帮助人类收集、储存和挖掘更多的地球数据,同时还提供了预测的工具。

9、交通行为预测

基于用户和车辆的LBS定位数据,分析人车出行的个体和群体特征,进行交通行为的预测。交通部门可预测不同时点不同道路的车流量进行智能的车辆调度,或应用潮汐车道;用户则可以根据预测结果选择拥堵几率更低的道路。

百度基于地图应用的LBS预测涵盖范围更广。春运期间预测人们的迁徙趋势指导火车线路和航线的设置,节假日预测景点的人流量指导人们的景区选择,平时还有百度热力图来告诉用户城市商圈、动物园等地点的人流情况,指导用户出行选择和商家的选点选址。

多尔戈夫的团队利用机器学习算法来创造路上行人的模型。无人驾驶汽车行驶的每一英里路程的情况都会被记录下来,汽车电脑就会保持这些数据,并分析各种不同的对象在不同的环境中如何表现。有些司机的行为可能会被设置为固定变量(如“绿灯亮,汽车行”),但是汽车电脑不会死搬硬套这种逻辑,而是从实际的司机行为中进行学习。

这样一来,跟在一辆垃圾运输卡车后面行驶的汽车,如果卡车停止行进,那么汽车可能会选择变道绕过去,而不是也跟着停下来。谷歌已建立了70万英里的行驶数据,这有助于谷歌汽车根据自己的学习经验来调整自己的行为。


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

http://www.5lian.cn/html/2014/chelianwang_0522/42125_4.html

10、能源消耗预测

加州电网系统运营中心管理着加州超过80%的电网,向3500万用户每年输送2.89亿兆瓦电力,电力线长度超过25000英里。该中心采用了Space-Time Insight的软件进行智能管理,综合分析来自包括天气、传感器、计量设备等各种数据源的海量数据,预测各地的能源需求变化,进行智能电能调度,平衡全网的电力供应和需求,并对潜在危机做出快速响应。中国智能电网业已在尝试类似大数据预测应用。

二、大数据分析种类 按照数据分析的实时性,分为实时数据分析和离线数据分析两种。

实时数据分析一般用于金融、移动和互联网B2C等产品,往往要求在数秒内返回上亿行数据的分析,从而达到不影响用户体验的目的。要满足这样的需求,可以采用精心设计的传统关系型数据库组成并行处理集群,或者采用一些内存计算平台,或者采用HDD的架构,这些无疑都需要比较高的软硬件成本。目前比较新的海量数据实时分析工具有EMC的Greenplum、SAP的HANA等。

对于大多数反馈时间要求不是那么严苛的应用,比如离线统计分析、机器学习、搜索引擎的反向索引计算、推荐引擎的计算等,应采用离线分析的方式,通过数据采集工具将日志数据导入专用的分析平台。但面对海量数据,传统的ETL工具往往彻底失效,主要原因是数据格式转换的开销太大,在性能上无法满足海量数据的采集需求。互联网企业的海量数据采集工具,有Facebook开源的Scribe、LinkedIn开源的Kafka、淘宝开源的Timetunnel、Hadoop的Chukwa等,均可以满足每秒数百MB的日志数据采集和传输需求,并将这些数据上载到Hadoop中央系统上。

按照大数据的数据量,分为内存级别、BI级别、海量级别三种。

这里的内存级别指的是数据量不超过集群的内存最大值。不要小看今天内存的容量,Facebook缓存在内存的Memcached中的数据高达320TB,而目前的PC服务器,内存也可以超过百GB。因此可以采用一些内存数据库,将热点数据常驻内存之中,从而取得非常快速的分析能力,非常适合实时分析业务。图1是一种实际可行的MongoDB分析架构。


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

图1 用于实时分析的MongoDB架构

MongoDB大集群目前存在一些稳定性问题,会发生周期性的写堵塞和主从同步失效,但仍不失为一种潜力十足的可以用于高速数据分析的NoSQL。

此外,目前大多数服务厂商都已经推出了带4GB以上SSD的解决方案,利用内存+SSD,也可以轻易达到内存分析的性能。随着SSD的发展,内存数据分析必然能得到更加广泛的应用。

BI级别指的是那些对于内存来说太大的数据量,但一般可以将其放入传统的BI产品和专门设计的BI数据库之中进行分析。目前主流的BI产品都有支持TB级以上的数据分析方案。种类繁多。

海量级别指的是对于数据库和BI产品已经完全失效或者成本过高的数据量。海量数据级别的优秀企业级产品也有很多,但基于软硬件的成本原因,目前大多数互联网企业采用Hadoop的HDFS分布式文件系统来存储数据,并使用MapReduce进行分析。本文稍后将主要介绍Hadoop上基于MapReduce的一个多维数据分析平台。

三、大数据分析一般过程

3.1 采集

大数据的采集是指利用多个数据库来接收发自客户端(Web、App或者传感器形式等)的 数据,并且用户可以通过这些数据库来进行简单的查询和处理工作。比如,电商会使用传统的关系型数据库mysql和Oracle等来存储每一笔事务数据,除 此之外,Redis和MongoDB这样的NoSQL数据库也常用于数据的采集。

在大数据的采集过程中,其主要特点和挑战是并发数高,因为同时有可能会有成千上万的用户 来进行访问和操作,比如火车票售票网站和淘宝,它们并发的访问量在峰值时达到上百万,所以需要在采集端部署大量数据库才能支撑。并且如何在这些数据库之间 进行负载均衡和分片的确是需要深入的思考和设计。

3.2 导入/预处理

虽然采集端本身会有很多数据库,但是如果要对这些海量数据进行有效的分析,还是应该将这 些来自前端的数据导入到一个集中的大型分布式数据库,或者分布式存储集群,并且可以在导入基础上做一些简单的清洗和预处理工作。也有一些用户会在导入时使 用来自Twitter的Storm来对数据进行流式计算,来满足部分业务的实时计算需求。
导入与预处理过程的特点和挑战主要是导入的数据量大,每秒钟的导入量经常会达到百兆,甚至千兆级别。

3.3 统计/分析

统计与分析主要利用分布式数据库,或者分布式计算集群来对存储于其内的海量数据进行普通 的分析和分类汇总等,以满足大多数常见的分析需求,在这方面,一些实时性需求会用到EMC的GreenPlum、Oracle的Exadata,以及基于 MySQL的列式存储Infobright等,而一些批处理,或者基于半结构化数据的需求可以使用Hadoop。
统计与分析这部分的主要特点和挑战是分析涉及的数据量大,其对系统资源,特别是I/O会有极大的占用。

3.4 挖掘

与前面统计和分析过程不同的是,数据挖掘一般没有什么预先设定好的主题,主要是在现有数 据上面进行基于各种算法的计算,从而起到预测(Predict)的效果,从而实现一些高级别数据分析的需求。比较典型算法有用于聚类的Kmeans、用于 统计学习的SVM和用于分类的NaiveBayes,主要使用的工具有Hadoop的Mahout等。该过程的特点和挑战主要是用于挖掘的算法很复杂,并 且计算涉及的数据量和计算量都很大,常用数据挖掘算法都以单线程为主。


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理
四、大数据分析工具

4.1 Hadoop

Hadoop 是一个能够对大量数据进行分布式处理的软件框架。但是 Hadoop 是以一种可靠、高效、可伸缩的方式进行处理的。Hadoop 是可靠的,因为它假设计算元素和存储会失败,因此它维护多个工作数据副本,确保能够针对失败的节点重新分布处理。Hadoop 是高效的,因为它以并行的方式工作,通过并行处理加快处理速度。Hadoop 还是可伸缩的,能够处理 PB 级数据。此外,Hadoop 依赖于社区服务器,因此它的成本比较低,任何人都可以使用。

Hadoop是一个能够让用户轻松架构和使用的分布式计算平台。用户可以轻松地在Hadoop上开发和运行处理海量数据的应用程序。它主要有以下几个优点:

高可靠性。Hadoop按位存储和处理数据的能力值得人们信赖。 高扩展性。Hadoop是在可用的计算机集簇间分配数据并完成计算任务的,这些集簇可以方便地扩展到数以千计的节点中。 高效性。Hadoop能够在节点之间动态地移动数据,并保证各个节点的动态平衡,因此处理速度非常快。 高容错性。Hadoop能够自动保存数据的多个副本,并且能够自动将失败的任务重新分配。

Hadoop带有用 Java 语言编写的框架,因此运行在 linux 生产平台上是非常理想的。Hadoop 上的应用程序也可以使用其他语言编写,比如 C++。

4.2 HPCC

HPCC,High Performance Computing and Communications(高性能计算与通信)的缩写。1993年,由美国科学、工程、技术联邦协调理事会向国会提交了“重大挑战项目:高性能计算与 通信”的报告,也就是被称为HPCC计划的报告,即美国总统科学战略项目,其目的是通过加强研究与开发解决一批重要的科学与技术挑战问题。HPCC是美国 实施信息高速公路而上实施的计划,该计划的实施将耗资百亿美元,其主要目标要达到:开发可扩展的计算系统及相关软件,以支持太位级网络传输性能,开发千兆 比特网络技术,扩展研究和教育机构及网络连接能力。

该项目主要由五部分组成:

高性能计算机系统(HPCS),内容包括今后几代计算机系统的研究、系统设计工具、先进的典型系统及原有系统的评价等; 先进软件技术与算法(ASTA),内容有巨大挑战问题的软件支撑、新算法设计、软件分支与工具、计算计算及高性能计算研究中心等; 国家科研与教育网格(NREN),内容有中接站及10亿位级传输的研究与开发; 基本研究与人类资源(BRHR),内容有基础研究、培训、教育及课程教材,被设计通过奖励调查者-开始的,长期 的调查在可升级的高性能计算中来增加创新意识流,通过提高教育和高性能的计算训练和通信来加大熟练的和训练有素的人员的联营,和来提供必需的基础架构来支 持这些调查和研究活动; 信息基础结构技术和应用(IITA),目的在于保证美国在先进信息技术开发方面的领先地位。

4.3 Storm

Storm是自由的开源软件,一个分布式的、容错的实时计算系统。Storm可以非常可靠的处理庞大的数据流,用于处理Hadoop的批量数据。Storm很简单,支持许多种编程语言,使用起来非常有趣。Storm由Twitter开源而来,其它知名的应用企业包括Groupon、淘宝、支付宝、阿里巴巴、乐元素、Admaster等等。

Storm有许多应用领域:实时分析、在线机器学习、不停顿的计算、分布式RPC(远过程调用协议,一种通过网络从远程计算机程序上请求服务)、 ETL(Extraction-Transformation-Loading的缩写,即数据抽取、转换和加载)等等。Storm的处理速度惊人:经测 试,每个节点每秒钟可以处理100万个数据元组。Storm是可扩展、容错,很容易设置和操作。

4.4 Apache Drill

为了帮助企业用户寻找更为有效、加快Hadoop数据查询的方法,Apache软件基金会近日发起了一项名为“Drill”的开源项目。Apache Drill 实现了 Google’s Dremel.

据Hadoop厂商MapRTechnologies公司产品经理Tomer Shiran介绍,“Drill”已经作为Apache孵化器项目来运作,将面向全球软件工程师持续推广。

该项目将会创建出开源版本的谷歌Dremel Hadoop工具(谷歌使用该工具来为Hadoop数据分析工具的互联网应用提速)。而“Drill”将有助于Hadoop用户实现更快查询海量数据集的目的。

“Drill”项目其实也是从谷歌的Dremel项目中获得灵感:该项目帮助谷歌实现海量数据集的分析处理,包括分析抓取Web文档、跟踪安装在Android Market上的应用程序数据、分析垃圾邮件、分析谷歌分布式构建系统上的测试结果等等。

通过开发“Drill”Apache开源项目,组织机构将有望建立Drill所属的API接口和灵活强大的体系架构,从而帮助支持广泛的数据源、数据格式和查询语言。

4.5 RapidMiner

RapidMiner是世界领先的数据挖掘解决方案,在一个非常大的程度上有着先进技术。它数据挖掘任务涉及范围广泛,包括各种数据艺术,能简化数据挖掘过程的设计和评价。

功能和特点

免费提供数据挖掘技术和库 100%用Java代码(可运行在操作系统) 数据挖掘过程简单,强大和直观 内部XML保证了标准化的格式来表示交换数据挖掘过程 可以用简单脚本语言自动进行大规模进程 多层次的数据视图,确保有效和透明的数据 图形用户界面的互动原型 命令行(批处理模式)自动大规模应用 Java API(应用编程接口) 简单的插件和推广机制 强大的可视化引擎,许多尖端的高维数据的可视化建模 400多个数据挖掘运营商支持

耶鲁大学已成功地应用在许多不同的应用领域,包括文本挖掘,多媒体挖掘,功能设计,数据流挖掘,集成开发的方法和分布式数据挖掘。

4.6 Pentaho BI

Pentaho BI 平台不同于传统的BI 产品,它是一个以流程为中心的,面向解决方案(Solution)的框架。其目的在于将一系列企业级BI产品、开源软件、API等等组件集成起来,方便商务智能应用的开发。它的出现,使得一系列的面向商务智能的独立产品如Jfree、Quartz等等,能够集成在一起,构成一项项复杂的、完整的商务智能解决方案。

Pentaho BI 平台,Pentaho Open BI 套件的核心架构和基础,是以流程为中心的,因为其中枢控制器是一个工作流引擎。工作流引擎使用流程定义来定义在BI 平台上执行的商业智能流程。流程可以很容易的被定制,也可以添加新的流程。BI 平台包含组件和报表,用以分析这些流程的性能。目前,Pentaho的主要组成元素包括报表生成、分析、数据挖掘和工作流管理等等。这些组件通过 J2EE、WebService、SOAP、HTTP、Java、javascript、Portals等技术集成到Pentaho平台中来。 Pentaho的发行,主要以Pentaho SDK的形式进行。

Pentaho SDK共包含五个部分:Pentaho平台、Pentaho示例数据库、可独立运行的Pentaho平台、Pentaho解决方案示例和一个预先配制好的 Pentaho网络服务器。其中Pentaho平台是Pentaho平台最主要的部分,囊括了Pentaho平台源代码的主体;Pentaho数据库为 Pentaho平台的正常运行提供的数据服务,包括配置信息、Solution相关的信息等等,对于Pentaho平台来说它不是必须的,通过配置是可以用其它数据库服务取代的;可独立运行的Pentaho平台是Pentaho平台的独立运行模式的示例,它演示了如何使Pentaho平台在没有应用服务器支持的情况下独立运行;

Pentaho解决方案示例是一个Eclipse工程,用来演示如何为Pentaho平台开发相关的商业智能解决方案。

Pentaho BI 平台构建于服务器,引擎和组件的基础之上。这些提供了系统的J2EE 服务器,安全,portal,工作流,规则引擎,图表,协作,内容管理,数据集成,分析和建模功能。这些组件的大部分是基于标准的,可使用其他产品替换之。

4.7 SAS Enterprise Miner

§ 支持整个数据挖掘过程的完备工具集 § 易用的图形界面,适合不同类型的用户快速建模 § 强大的模型管理和评估功能 § 快速便捷的模型发布机制, 促进业务闭环形成 五、数据分析算法

大数据分析主要依靠机器学习和大规模计算。机器学习包括监督学习、非监督学习、强化学习等,而监督学习又包括分类学习、回归学习、排序学习、匹配学习等(见图1)。分类是最常见的机器学习应用问题,比如垃圾邮件过滤、人脸检测、用户画像、文本情感分析、网页归类等,本质上都是分类问题。分类学习也是机器学习领域,研究最彻底、使用最广泛的一个分支。

最近、Fernández-Delgado等人在JMLR(Journal of Machine Learning Research,机器学习顶级期刊)杂志发表了一篇有趣的论文。他们让179种不同的分类学习方法(分类学习算法)在UCI 121个数据集上进行了“大比武”(UCI是机器学习公用数据集,每个数据集的规模都不大)。结果发现Random Forest(随机森林)和SVM(支持向量机)名列第一、第二名,但两者差异不大。在84.3%的数据上、Random Forest压倒了其它90%的方法。也就是说,在大多数情况下,只用Random Forest 或 SVM事情就搞定了。


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

https://github.com/linyiqun/DataMiningAlgorithm

KNN

K最近邻算法。给定一些已经训练好的数据,输入一个新的测试数据点,计算包含于此测试数据点的最近的点的分类情况,哪个分类的类型占多数,则此测试点的分类与此相同,所以在这里,有的时候可以复制不同的分类点不同的权重。近的点的权重大点,远的点自然就小点。详细介绍链接

Naive Bayes

朴素贝叶斯算法。朴素贝叶斯算法是贝叶斯算法里面一种比较简单的分类算法,用到了一个比较重要的贝叶斯定理,用一句简单的话概括就是条件概率的相互转换推导。详细介绍链接

朴素贝叶斯分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解在此项出现的条件下各个类别出现的概率,哪个最大,就认为此待分类项属于哪个类别。通俗来说,就好比这么个道理,你在街上看到一个黑人,我问你你猜这哥们哪里来的,你十有八九猜非洲。为什么呢?因为黑人中非洲人的比率最高,当然人家也可能是美洲人或亚洲人,但在没有其它可用信息下,我们会选择条件概率最大的类别,这就是朴素贝叶斯的思想基础。

SVM

支持向量机算法。支持向量机算法是一种对线性和非线性数据进行分类的方法,非线性数据进行分类的时候可以通过核函数转为线性的情况再处理。其中的一个关键的步骤是搜索最大边缘超平面。详细介绍链接

Apriori

Apriori算法是关联规则挖掘算法,通过连接和剪枝运算挖掘出频繁项集,然后根据频繁项集得到关联规则,关联规则的导出需要满足最小置信度的要求。详细介绍链接

PageRank

网页重要性/排名算法。PageRank算法最早产生于Google,核心思想是通过网页的入链数作为一个网页好快的判定标准,如果1个网页内部包含了多个指向外部的链接,则PR值将会被均分,PageRank算法也会遭到LinkSpan攻击。详细介绍链接

RandomForest

随机森林算法。算法思想是决策树+boosting.决策树采用的是CART分类回归数,通过组合各个决策树的弱分类器,构成一个最终的强分类器,在构造决策树的时候采取随机数量的样本数和随机的部分属性进行子决策树的构建,避免了过分拟合的现象发生。详细介绍链接

Artificial Neural Network

“神经网络”这个词实际是来自于生物学,而我们所指的神经网络正确的名称应该是“人工神经网络(ANNs)”。
人工神经网络也具有初步的自适应与自组织能力。在学习或训练过程中改变突触权重值,以适应周围环境的要求。同一网络因学习方式及内容不同可具有不同的功能。人工神经网络是一个具有学习能力的系统,可以发展知识,以致超过设计者原有的知识水平。通常,它的学习训练方式可分为两种,一种是有监督或称有导师的学习,这时利用给定的样本标准进行分类或模仿;另一种是无监督学习或称无为导师学习,这时,只规定学习方式或某些规则,则具体的学习内容随系统所处环境 (即输入信号情况)而异,系统可以自动发现环境特征和规律性,具有更近似人脑的功能。 六、 案例

6.1 啤酒与尿布


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

“啤酒与尿布”的故事产生于20世纪90年代的美国沃尔玛超市中,沃尔玛的超市管理人员分析销售数据时发现了一个令人难于理解的现象:在某些特定的情况下,“啤酒”与“尿布”两件看上去毫无关系的商品会经常出现在同一个购物篮中,这种独特的销售现象引起了管理人员的注意,经过后续调查发现,这种现象出现在年轻的父亲身上。

在美国有婴儿的家庭中,一般是母亲在家中照看婴儿,年轻的父亲前去超市购买尿布。父亲在购买尿布的同时,往往会顺便为自己购买啤酒,这样就会出现啤酒与尿布这两件看上去不相干的商品经常会出现在同一个购物篮的现象。如果这个年轻的父亲在卖场只能买到两件商品之一,则他很有可能会放弃购物而到另一家商店, 直到可以一次同时买到啤酒与尿布为止。沃尔玛发现了这一独特的现象,开始在卖场尝试将啤酒与尿布摆放在相同的区域,让年轻的父亲可以同时找到这两件商品,并很快地完成购物;而沃尔玛超市也可以让这些客户一次购买两件商品、而不是一件,从而获得了很好的商品销售收入,这就是“啤酒与尿布” 故事的由来。

当然“啤酒与尿布”的故事必须具有技术方面的支持。1993年美国学者Agrawal提出通过分析购物篮中的商品集合,从而找出商品之间关联关系的关联算法,并根据商品之间的关系,找出客户的购买行为。艾格拉沃从数学及计算机算法角度提 出了商品关联关系的计算方法——Aprior算法。沃尔玛从上个世纪 90 年代尝试将 Aprior算法引入到 POS机数据分析中,并获得了成功,于是产生了“啤酒与尿布”的故事。

6.2 数据分析帮助辛辛那提动物园提高客户满意度


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

辛辛那提动植物园成立于1873年,是世界上著名的动植物园之一,以其物种保护和保存以及高成活率繁殖饲养计划享有极高声誉。它占地面积71英亩,园内有500种动物和3000多种植物,是国内游客人数最多的动植物园之一,曾荣获Zagat十佳动物园,并被《父母》(Parent)杂志评为最受儿童喜欢的动物园,每年接待游客130多万人。

辛辛那提动植物园是一个非营利性组织,是俄亥州同时也是美国国内享受公共补贴最低的动植物园,除去政府补贴,2600万美元年度预算中,自筹资金部分达到三分之二以上。为此,需要不断地寻求增加收入。而要做到这一点,最好办法是为工作人员和游客提供更好的服务,提高游览率。从而实现动植物园与客户和纳税人的双赢。

借助于该方案强大的收集和处理能力、互联能力、分析能力以及随之带来的洞察力,在部署后,企业实现了以下各方面的受益: 帮助动植物园了解每个客户浏览、使用和消费模式,根据时间和地理分布情况采取相应的措施改善游客体验,同时实现营业收入最大化。 根据消费和游览行为对动植物园游客进行细分,针对每一类细分游客开展营销和促销活动,显著提高忠诚度和客户保有量。. 识别消费支出低的游客,针对他们发送具有战略性的直寄广告,同时通过具有创意性的营销和激励计划奖励忠诚客户。 360度全方位了解客户行为,优化营销决策,实施解决方案后头一年节省40,000多美元营销成本,同时强化了可测量的结果。 采用地理分析显示大量未实现预期结果的促销和折扣计划,重新部署资源支持产出率更高的业务活动,动植物园每年节省100,000多美元。 通过强化营销提高整体游览率,2011年至少新增50,000人次“游览”。 提供洞察结果强化运营管理。例如,即将关门前冰激淋销售出现高潮,动植物园决定延长冰激淋摊位营业时间,直到关门为止。这一措施夏季每天可增加2,000美元收入。 与上年相比,餐饮销售增加30.7%,零售销售增加5.9%。 动植物园高层管理团队可以制定更好的决策,不需要 IT 介入或提供支持。 将分析引入会议室,利用直观工具帮助业务人员掌握数据。

6.3 云南昭通警察打中学生事件舆情分析

起因:

5月20日,有网友在微博上爆料称:云南昭通鲁甸二中初二学生孔德政,对着3名到该校出警并准备上车返回的警察说了一句“打电话那个,下来”,车内的两名警员听到动静后下来,追到该学生后就是一顿拳打脚踢。

5月26日,昭通市鲁甸县公安局新闻办回应此事:鲁甸县公安局已对当事民警停止执行职务,对殴打学生的两名协警作出辞退处理,并将根据调查情况依法依规作进一步处理。同时,鲁甸县公安局将加大队伍教育管理力度,坚决防止此类事件的再次发生。

经过:


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

5月26日,事件的舆情热度急剧上升,媒体报道内容侧重于“班主任称此学生平时爱起哄学习成绩差”“被打学生的同学去派出所讨说法”“学校要求学生删除照片”等方面,而学校要求删除图片等行为的曝光让事件舆情有扩大化趋势。

5月26日晚间,新华网发布新闻《警方回应“云南一学生遭2名警察暴打”:民警停职协警辞退》,中央主流网络媒体公布官方处置结果,网易、新浪、腾讯等门户网站予以转发,从而让官方的处置得以较大范围传播。


大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

昭通警察打中学生事件舆论关注度走势(抽样条数:290条)

总结:

“警察打学生,而且有图有真相,在事发5天后,昭通市鲁甸县警方最终还是站在了舆论的风口浪尖。事发后当地官方积极回应,并于5月26日将涉事人予以处理,果断的责任切割较为有效地抚平了舆论情绪,从而较好地化解了此次舆论危机。

从事件的传播来看,事发时间是5月20日,舆论热议则出现在25日,4天的平静期让鲁甸警方想当然地以为事件就此了结,或许当事人都已淡忘此事。如果不是云南当地活跃网友“直播云南”于5月25日发布关于此事的消息,并被当地传统媒体《生活新报》关注的话,事情或许真的就此结束,然而舆情发展不允许假设的存在。这一点,至少给我们以警示,对微博等自媒体平台上的负面信息要实时监测,对普通草根要监测,对本地实名认证的活跃网友更需监测。从某种角度看,本地实名认证的网友是更为强大的“舆论发动机”,负面消息一旦经他们发布或者转发,所带来的传播和形成的舆论压力更大。

在此事件中,校方也扮演着极为重要的角色。无论是被打学生的班主任,还是学校层面,面对此事件的回应都欠妥当。学校层面的“删除照片”等指示极易招致网友和学生的反感,在此反感情绪下,只会加剧学生传播事件的冲动。班主任口中该学生“学习不好、爱起哄”等负面印象被理解成“该学生活该被打”,在教师整体形象不佳的背景下,班主任的这些言论是责任感缺失的一种体现。校方和班主任的不恰当行为让事件处置难度和舆论引导难度明显增加,实在不该。“ — 人民网舆情监测室主任舆情分析师朱明刚

七、大数据云图展示
大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理
大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理
大数据就在你身边 | 生活中大数据分析案例以及背后的技术原理

End.

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          MIT CSAIL research offers a fully automated way to peer inside neural nets   
 MIT’s Computer Science and Artificial Intelligence Lab has devised a way to look inside neural networks and shed some light on how they’re actually making decisions. The new process is a fully automated version of the system the research team behind it presented two years ago, which employed human reviewers to achieve the same ends. Coming up with a method that can provide… Read More

          The Ultimate Data Infrastructure Architect Bundle for $36   
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Learning ElasticSearch 5.0


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Learn how to use ElasticSearch in combination with the rest of the Elastic Stack to ship, parse, store, and analyze logs! You'll start by getting an understanding of what ElasticSearch is, what it's used for, and why it's important before being introduced to the new features of Elastic Search 5.0.

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Ethan Anthony is a San Francisco based Data Scientist who specializes in distributed data centric technologies. He is also the Founder of XResults, where the vision is to harness the power of data to innovate and deliver intuitive customer facing solutions, largely to non-technical professionals. Ethan has over 10 combined years of experience in cloud based technologies such as Amazon webservices and OpenStack, as well as the data centric technologies of Hadoop, Mahout, Spark and ElasticSearch. He began using ElasticSearch in 2011 and has since delivered solutions based on the Elastic Stack to a broad range of clientele. Ethan has also consulted worldwide, speaks fluent Mandarin Chinese and is insanely curious about human cognition, as related to cognitive dissonance.

Apache Spark 2 for Beginners


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Apache Spark is one of the most widely-used large-scale data processing engines and runs at extremely high speeds. It's a framework that has tools that are equally useful for app developers and data scientists. This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup.

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Rajanarayanan Thottuvaikkatumana, Raj, is a seasoned technologist with more than 23 years of software development experience at various multinational companies. He has lived and worked in India, Singapore, and the USA, and is presently based out of the UK. His experience includes architecting, designing, and developing software applications. He has worked on various technologies including major databases, application development platforms, web technologies, and big data technologies. Since 2000, he has been working mainly in Java related technologies, and does heavy-duty server-side programming in Java and Scala. He has worked on very highly concurrent, highly distributed, and high transaction volume systems. Currently he is building a next generation Hadoop YARN-based data processing platform and an application suite built with Spark using Scala.

Raj holds one master's degree in Mathematics, one master's degree in Computer Information Systems and has many certifications in ITIL and cloud computing to his credit. Raj is the author of Cassandra Design Patterns - Second Edition, published by Packt.

When not working on the assignments his day job demands, Raj is an avid listener to classical music and watches a lot of tennis.

Designing AWS Environments


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Amazon Web Services (AWS) provides trusted, cloud-based solutions to help businesses meet all of their needs. Running solutions in the AWS Cloud can help you (or your company) get applications up and running faster while providing the security needed to meet your compliance requirements. This course leaves no stone unturned in getting you up to speed with administering AWS.

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Wayde Gilchrist started moving customers of his IT consulting business into the cloud and away from traditional hosting environments in 2010. In addition to consulting, he delivers AWS training for Fortune 500 companies, government agencies, and international consulting firms. When he is not out visiting customers, he is delivering training virtually from his home in Florida.

Learning MongoDB


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Businesses today have access to more data than ever before, and a key challenge is ensuring that data can be easily accessed and used efficiently. MongoDB makes it possible to store and process large sets of data in a ways that drive up business value. Learning MongoDB will give you the flexibility of unstructured storage, combined with robust querying and post processing functionality, making you an asset to enterprise Big Data needs.

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  • Master data management, queries, post processing, & essential enterprise redundancy requirements
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Daniel Watrous is a 15-year veteran of designing web-enabled software. His focus on data store technologies spans relational databases, caching systems, and contemporary NoSQL stores. For the last six years, he has designed and deployed enterprise-scale MongoDB solutions in semiconductor manufacturing and information technology companies. He holds a degree in electrical engineering from the University of Utah, focusing on semiconductor physics and optoelectronics. He also completed an MBA from the Northwest Nazarene University. In his current position as senior cloud architect with Hewlett Packard, he focuses on highly scalable cloud-native software systems.

Learning Hadoop 2


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Hadoop emerged in response to the proliferation of masses and masses of data collected by organizations, offering a strong solution to store, process, and analyze what has commonly become known as Big Data. It comprises a comprehensive stack of components designed to enable these tasks on a distributed scale, across multiple servers and thousand of machines. In this course, you'll learn Hadoop 2, introducing yourself to the powerful system synonymous with Big Data.

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Randal Scott King is the Managing Partner of Brilliant Data, a consulting firm specialized in data analytics. In his 16 years of consulting, Scott has amassed an impressive list of clientele from mid-market leaders to Fortune 500 household names. Scott lives just outside Atlanta, GA, with his children.

ElasticSearch 5.x Cookbook eBook


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ElasticSearch is a Lucene-based distributed search server that allows users to index and search unstructured content with petabytes of data. Through this ebook, you'll be guided through comprehensive recipes covering what's new in ElasticSearch 5.x as you create complex queries and analytics. By the end, you'll have an in-depth knowledge of how to implement the ElasticSearch architecture and be able to manage data efficiently and effectively.

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  • Perform index mapping, aggregation, & scripting
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Alberto Paro is an engineer, project manager, and software developer. He currently works as freelance trainer/consultant on big data technologies and NoSQL solutions. He loves to study emerging solutions and applications mainly related to big data processing, NoSQL, natural language processing, and neural networks. He began programming in BASIC on a Sinclair Spectrum when he was eight years old, and to date, has collected a lot of experience using different operating systems, applications, and programming languages.

In 2000, he graduated in computer science engineering from Politecnico di Milano with a thesis on designing multiuser and multidevice web applications. He assisted professors at the university for about a year. He then came in contact with The Net Planet Company and loved their innovative ideas; he started working on knowledge management solutions and advanced data mining products. In summer 2014, his company was acquired by a big data technologies company, where he worked until the end of 2015 mainly using Scala and Python on state-of-the-art big data software (Spark, Akka, Cassandra, and YARN). In 2013, he started freelancing as a consultant for big data, machine learning, Elasticsearch and other NoSQL products. He has created or helped to develop big data solutions for business intelligence, financial, and banking companies all over the world. A lot of his time is spent teaching how to efficiently use big data solutions (mainly Apache Spark), NoSql datastores (Elasticsearch, HBase, and Accumulo) and related technologies (Scala, Akka, and Playframework). He is often called to present at big data or Scala events. He is an evangelist on Scala and Scala.js (the transcompiler from Scala to JavaScript).

In his spare time, when he is not playing with his children, he likes to work on open source projects. When he was in high school, he started contributing to projects related to the GNOME environment (gtkmm). One of his preferred programming languages is Python, and he wrote one of the first NoSQL backends on Django for MongoDB (Django-MongoDBengine). In 2010, he began using Elasticsearch to provide search capabilities to some Django e-commerce sites and developed PyES (a Pythonic client for Elasticsearch), as well as the initial part of the Elasticsearch MongoDB river. He is the author of Elasticsearch Cookbook as well as a technical reviewer of Elasticsearch Server-Second Edition, Learning Scala Web Development, and the video course, Building a Search Server with Elasticsearch, all of which are published by Packt Publishing.

Fast Data Processing with Spark 2 eBook


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Compared to Hadoop, Spark is a significantly more simple way to process Big Data at speed. It is increasing in popularity with data analysts and engineers everywhere, and in this course you'll learn how to use Spark with minimum fuss. Starting with the fundamentals, this ebook will help you take your Big Data analytical skills to the next level.

  • Access 274 pages of content 24/7
  • Get to grips w/ some simple APIs before investigating machine learning & graph processing
  • Learn how to use the Spark shell
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Krishna Sankar is a Senior Specialist—AI Data Scientist with Volvo Cars focusing on Autonomous Vehicles. His earlier stints include Chief Data Scientist at http://cadenttech.tv/, Principal Architect/Data Scientist at Tata America Intl. Corp., Director of Data Science at a bioinformatics startup, and as a Distinguished Engineer at Cisco. He has been speaking at various conferences including ML tutorials at Strata SJC and London 2016, Spark Summit, Strata-Spark Camp, OSCON, PyCon, and PyData, writes about Robots Rules of Order, Big Data Analytics—Best of the Worst, predicting NFL, Spark, Data Science, Machine Learning, Social Media Analysis as well as has been a guest lecturer at the Naval Postgraduate School. His occasional blogs can be found at https://doubleclix.wordpress.com/. His other passion is flying drones (working towards Drone Pilot License (FAA UAS Pilot) and Lego Robotics—you will find him at the St.Louis FLL World Competition as Robots Design Judge.

MongoDB Cookbook: Second Edition eBook


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MongoDB is a high-performance, feature-rich, NoSQL database that forms the backbone of the systems that power many organizations. Packed with easy-to-use features that have become essential for a variety of software professionals, MongoDB is a vital technology to learn for any aspiring data scientist or systems engineer. This cookbook contains many solutions to the everyday challenges of MongoDB, as well as guidance on effective techniques to extend your skills and capabilities.

  • Access 274 pages of content 24/7
  • Initialize the server in three different modes w/ various configurations
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Amol Nayak is a MongoDB certified developer and has been working as a developer for over 8 years. He is currently employed with a leading financial data provider, working on cutting-edge technologies. He has used MongoDB as a database for various systems at his current and previous workplaces to support enormous data volumes. He is an open source enthusiast and supports it by contributing to open source frameworks and promoting them. He has made contributions to the Spring Integration project, and his contributions are the adapters for JPA, XQuery, MongoDB, Push notifications to mobile devices, and Amazon Web Services (AWS). He has also made some contributions to the Spring Data MongoDB project. Apart from technology, he is passionate about motor sports and is a race official at Buddh International Circuit, India, for various motor sports events. Earlier, he was the author of Instant MongoDB, Packt Publishing.

Cyrus Dasadia always liked tinkering with open source projects since 1996. He has been working as a Linux system administrator and part-time programmer for over a decade. He works at InMobi, where he loves designing tools and platforms. His love for MongoDB started in 2013, when he was amazed by its ease of use and stability. Since then, almost all of his projects are written with MongoDB as the primary backend. Cyrus is also the creator of an open source alert management system called CitoEngine. He likes spending his spare time trying to reverse engineer software, playing computer games, or increasing his silliness quotient by watching reruns of Monty Python.

Learning Apache Kafka: Second Edition eBook


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Apache Kafka is simple describe at a high level bust has an immense amount of technical detail when you dig deeper. This step-by-step, practical guide will help you take advantage of the power of Kafka to handle hundreds of megabytes of messages per second from multiple clients.

  • Access 120 pages of content 24/7
  • Set up Kafka clusters
  • Understand basic blocks like producer, broker, & consumer blocks
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  • Discover how Kafka works w/ other tools like Hadoop, Storm, & more

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Nishant Garg has over 14 years of software architecture and development experience in various technologies, such as Java Enterprise Edition, SOA, Spring, Hadoop, Hive, Flume, Sqoop, Oozie, Spark, Shark, YARN, Impala, Kafka, Storm, Solr/Lucene, NoSQL databases (such as HBase, Cassandra, and MongoDB), and MPP databases (such as GreenPlum).

He received his MS in software systems from the Birla Institute of Technology and Science, Pilani, India, and is currently working as a technical architect for the Big Data R&D Group with Impetus Infotech Pvt. Ltd. Previously, Nishant has enjoyed working with some of the most recognizable names in IT services and financial industries, employing full software life cycle methodologies such as Agile and SCRUM.

Nishant has also undertaken many speaking engagements on big data technologies and is also the author of HBase Essestials, Packt Publishing.

Apache Flume: Distributed Log Collection for Hadoop: Second Edition eBook


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Apache Flume is a distributed, reliable, and available service used to efficiently collect, aggregate, and move large amounts of log data. It's used to stream logs from application servers to HDFS for ad hoc analysis. This ebook start with an architectural overview of Flume and its logical components, and pulls everything together into a real-world, end-to-end use case encompassing simple and advanced features.

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  • Explore channels, sinks, & sink processors
  • Learn about sources & channels
  • Construct a series of Flume agents to dynamically transport your stream data & logs from your systems into Hadoop

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Steve Hoffman has 32 years of experience in software development, ranging from embedded software development to the design and implementation of large-scale, service-oriented, object-oriented systems. For the last 5 years, he has focused on infrastructure as code, including automated Hadoop and HBase implementations and data ingestion using Apache Flume. Steve holds a BS in computer engineering from the University of Illinois at Urbana-Champaign and an MS in computer science from DePaul University. He is currently a senior principal engineer at Orbitz Worldwide (http://orbitz.com/).

          Maybe Trump’s behavior is explained by a simple Machine Learning (A.I.) algorithm.    
Burton offers an intriguing explanation for our inability to predict Donald Trump’s next move suggesting:
...that Trump doesn’t operate within conventional human cognitive constraints, but rather is a new life form, a rudimentary artificial intelligence-based learning machine. When we strip away all moral, ethical and ideological considerations from his decisions and see them strictly in the light of machine learning, his behavior makes perfect sense.
Consider how deep learning occurs in neural networks such as Google’s Deep Mind or IBM’s Deep Blue and Watson. In the beginning, each network analyzes a number of previously recorded games, and then, through trial and error, the network tests out various strategies. Connections for winning moves are enhanced; losing connections are pruned away. The network has no idea what it is doing or why one play is better than another. It isn’t saddled with any confounding principles such as what constitutes socially acceptable or unacceptable behavior or which decisions might result in negative downstream consequences.
Now up the stakes…ask a neural network to figure out the optimal strategy…for the United States presidency. In this hypothetical, let’s input and analyze all available written and spoken word — from mainstream media commentary to the most obscure one-off crank pamphlets. After running simulations of various hypotheses, the network will serve up its suggestions. It might show Trump which areas of the country are most likely to respond to personal appearances, which rallies and town hall meetings will generate the greatest photo op and TV coverage, and which publicly manifest personality traits will garner the most votes. If it determines that outrage is the only road to the presidency, it will tell Trump when and where his opinions must be scandalous and offensively polarizing.
Following the successful election, it chews on new data. When it recognizes that Obamacare won’t be easily repealed or replaced, that token intervention in Syria can’t be avoided, that NATO is a necessity and that pulling out of the Paris climate accord may create worldwide resentment, it has no qualms about changing policies and priorities. From an A.I. vantage point, the absence of a coherent agenda is entirely understandable. For example, a consistent long-term foreign policy requires a steadfastness contrary to a learning machine’s constant upgrading in response to new data.
As there are no lines of reasoning driving the network’s actions, it is not possible to reverse engineer the network to reveal the “why” of any decision. Asking why a network chose a particular action is like asking why Amazon might recommend James Ellroy and Elmore Leonard novels to someone who has just purchased “Crime and Punishment.” There is no underlying understanding of the nature of the books; the association is strictly a matter of analyzing Amazon’s click and purchase data. Without explanatory reasoning driving decision making, counterarguments become irrelevant.
Once we accept that Donald Trump represents a black-box, first-generation artificial-intelligence president driven solely by self-selected data and widely fluctuating criteria of success, we can get down to the really hard question confronting our collective future: Is there a way to affect changes in a machine devoid of the common features that bind humanity?

          Technology Associate, Team Lead for ATS Principal Based Strategies - Morgan Stanley - New York, NY   
Machine Learning including Regression and Clustering techniques, Support Vector Machines, Neural Networks, Probabilistic Graphical Models, and Econometric;...
From Morgan Stanley - Mon, 22 May 2017 20:45:35 GMT - View all New York, NY jobs
          Technology Associate, Team Lead for ATS Principal Based Strategies - Morgan Stanley - New York, NY   
Machine Learning including Regression and Clustering techniques, Support Vector Machines, Neural Networks, Probabilistic Graphical Models, and Econometric;...
From Morgan Stanley - Mon, 22 May 2017 20:45:35 GMT - View all New York, NY jobs
          Measurement of Tau Neutrino Appearance and Charged-current Tau Neutrino Cross Section with Atmospheric Neutrinos in Super-Kamiokande   

by: Li, Zepeng (Duke U.)

Abstract:
Super-Kamiokande is a 50 kiloton water-Cherenkov detector in Japan, which has been collecting atmospheric neutrino data for more than 20 years. Tau neutrino appearance is expected in atmospheric neutrinos due to neutrino oscillations. The wide span of neutrino energies and neutrino path lengths in atmospheric neutrinos and the large target mass of Super-Kamiokande allow a detection of charged-current tau neutrino interactions in Super-Kamiokande. This thesis describes a search for atmospheric tau neutrino appearance and a measurement of the charged-current tau neutrino cross section in Super-Kamiokande. A neural network is applied to identify charged-current tau neutrino interactions contained in the Super-Kamiokande detector. Using 5,326 days of atmospheric neutrino data, Super-K measures the tau normalization to be 1.47$\pm$0.32 under the assumption of the normal hierarchy, relative to the expectation of unity for nominal oscillation parameters and assumed charged-current tau neutrino cross section. The result excludes the hypothesis of no-tau-appearance with a significance level of 4.6$\sigma$, thus giving a direct evidence of neutrino flavor change due to neutrino oscillations. By scaling the cross sections in the simulations to match Super-K data, a flux-averaged charged-current tau neutrino cross section is measured to be $(0.94\pm0.20)\times 10^{-38}$ cm$^{2}$, compared with the theoretical flux-averaged charged-current tau neutrino cross section of $0.64\times 10^{-38}$ cm$^{2}$. This is the second reported measurement of this process. The measured cross section is consistent with the Standard Model prediction.
          Per Bak and Kim Sneppen Model   

The Per Bak and Kim Sneppen evolution model is one of the most simple and at the same time more elegant ways to show self-organised criticality. The above demo is part of a vaster set of animations illustrating complex systems that I’m in the process of coding in javascript and P5. I had coded a […]

The post Per Bak and Kim Sneppen Model appeared first on sixhat.net.


          MIT CSAIL research offers a fully automated way to peer inside neural nets   
 MIT’s Computer Science and Artificial Intelligence Lab has devised a way to look inside neural networks and shed some light on how they’re actually making decisions. The new process is a fully automated version of the system the research team behind it presented two years ago, which employed human reviewers to achieve the same ends. Coming up with a method that can provide… Read More

          Comment on Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras by Jason Brownlee   
Perhaps, I'm not sure I understand your dataset. Can you give a one-case example?
          Comment on How to Handle Very Long Sequences with Long Short-Term Memory Recurrent Neural Networks by Jason Brownlee   
Yes, I would recommend encoding the integers with a one hot encoding as long as you know the scope of input/output.
          Blitzkrieg 3 - Trailer (Boris Neural Network AI)   
Das Entwicklerstudio Nival stellt "Boris" vor: Das ist der Name der angeblich ersten echten Neuronale-Netzwerke-KI für ein Strategiespiel. Sie kämpft in Blitzkrieg 2, das im Zweiten Weltkrieg angesiedelt ist.
          Crazy Stone Deep Learning -The First Edition   
Crazy Stone nutzt Deep Neural Networks für die Computerspieler-KI.
          AI helps to fight against lung cancer   
Lung cancer has been the leading cause of cancer-related deaths in 2015 in United States. Early detection of lung nodules will undoubtedly increase the five-year survival rate for lung cancer according to prior studies. In a paper published in SCIENCE CHINA Information Sciences, researchers propose a novel rating method based on geometrical and statistical features to extract initial nodule candidates and an artificial neural network approach to the detection of lung nodules.
          Understanding the Mechanisms of Deep Transfer Learning for Medical Images   
This paper systematically studies how a convolutional neural network, trained on ImageNet for image classification tasks, works on medical images - or more precisely on ultrasound images - for the "Kidney Detection" problem.
          Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines. (arXiv:1706.09667v1 [cs.IT])   

Authors: Maxinder S. Kanwal, Joshua A. Grochow, Nihat Ay

In the past three decades, many theoretical measures of complexity have been proposed to help understand complex systems. In this work, for the first time, we place these measures on a level playing field, to explore the qualitative similarities and differences between them, and their shortcomings. Specifically, using the Boltzmann machine architecture (a fully connected recurrent neural network) with uniformly distributed weights as our model of study, we numerically measure how complexity changes as a function of network dynamics and network parameters. We apply an extension of one such information-theoretic measure of complexity to understand incremental Hebbian learning in Hopfield networks, a fully recurrent architecture model of autoassociative memory. In the course of Hebbian learning, the total information flow reflects a natural upward trend in complexity as the network attempts to learn more and more patterns.


          A Random Matrix Approach to Neural Networks. (arXiv:1702.05419v2 [math.PR] UPDATED)   

Authors: Cosme Louart, Zhenyu Liao, Romain Couillet

This article studies the Gram random matrix model $G=\frac1T\Sigma^{\rm T}\Sigma$, $\Sigma=\sigma(WX)$, classically found in the analysis of random feature maps and random neural networks, where $X=[x_1,\ldots,x_T]\in{\mathbb R}^{p\times T}$ is a (data) matrix of bounded norm, $W\in{\mathbb R}^{n\times p}$ is a matrix of independent zero-mean unit variance entries, and $\sigma:{\mathbb R}\to{\mathbb R}$ is a Lipschitz continuous (activation) function --- $\sigma(WX)$ being understood entry-wise. By means of a key concentration of measure lemma arising from non-asymptotic random matrix arguments, we prove that, as $n,p,T$ grow large at the same rate, the resolvent $Q=(G+\gamma I_T)^{-1}$, for $\gamma>0$, has a similar behavior as that met in sample covariance matrix models, involving notably the moment $\Phi=\frac{T}n{\mathbb E}[G]$, which provides in passing a deterministic equivalent for the empirical spectral measure of $G$. Application-wise, this result enables the estimation of the asymptotic performance of single-layer random neural networks. This in turn provides practical insights into the underlying mechanisms into play in random neural networks, entailing several unexpected consequences, as well as a fast practical means to tune the network hyperparameters.


          Material Discovery by Combining Stochastic Surface Walking Global Optimization with Neural Network   
Chem. Sci., 2017, Accepted Manuscript
DOI: 10.1039/C7SC01459G, Edge Article
Open Access Open Access
Creative Commons Licence  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
Si-Da Huang, Cheng Shang, Xiao-Jie Zhang, Zhipan Liu
While the underlying potential energy surface (PES) determines the structure and other properties of material, it has been frustrated to predict new materials from theory even with the advent of...
The content of this RSS Feed (c) The Royal Society of Chemistry

          Learning to Learn without Gradient Descent by Gradient Descent   

Learning to Learn without Gradient Descentby Gradient Descent by Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas 


We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to tradeoff exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.



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          MIT CSAIL research offers a fully automated way to peer inside neural nets   
 MIT’s Computer Science and Artificial Intelligence Lab has devised a way to look inside neural networks and shed some light on how they’re actually making decisions. The new process is a fully automated version of the system the research team behind it presented two years ago, which employed human reviewers to achieve the same ends. Coming up with a method that can provide… Read More
           Neural network data Design : A PCB Defects Classification Applications    
Heriansyah, Rudi and Syed Abu Bakar, Syed Abdul Rahman and Ahmad Zabidi, Muhammad Mun'im (2003) Neural network data Design : A PCB Defects Classification Applications. In: Seminar on Artificial Intelligence Applications in Industry , 2003, Kuala Lumpur.
           Towards a biological more plausible artificial neural networks    
Mohammed Amin, Muhamad Kamal (2012) Towards a biological more plausible artificial neural networks. In: Asia Simulation Conference (AsiaSim) 2012 and The International Conference on System Simulation and Scientific Computing 2012 (ICSC'2012), 27-30 October, 2012, Shanghai, China.
           Self-organized spiking neural network model for data clustering    
Mohammed Amin, Muhamad Kamal (2012) Self-organized spiking neural network model for data clustering. In: JSST 2012 International Conference on Simulation Technology, 27-28 September, 2012, Kobe, Japan.
           Adaptive neural network classifier for extracted invariants of handwritten digits    
Shamsuddin, Siti Mariyam and Keng, L. H. (2004) Adaptive neural network classifier for extracted invariants of handwritten digits. Journal of ICT, 3 (1). pp. 1-17. ISSN 1675-414X
           Transmission usage allocation in bilateral energy transaction using artificial neural network    
Mustafa, Mohd. Wazir and Shareef, Hussain and Abd. Khalid, Saifulnizam and Khairuddin, Azhar (2008) Transmission usage allocation in bilateral energy transaction using artificial neural network. In: Power Flow Allocation Approaches In Deregulated Power System. Penerbit UTM, Johor, pp. 71-90. ISBN 978-983-52-0679-5
           Supervised neural networks for time series data reconstruction    
Abdul Malek, Marlinda and Harun, Sobri and Mohamad, Ismail (2008) Supervised neural networks for time series data reconstruction. In: Modeling Techniques Of Rainfall And River Flow And Groundwater Contamination. Penerbit UTM, Johor, pp. 87-106.
           Process tomography system by lectrostatic charge carried by particles using neural network as flow identification tools    
Rahmat, Mohd. Fuaad and Sabit, Hakilo Ahmed (2007) Process tomography system by lectrostatic charge carried by particles using neural network as flow identification tools. In: Malaysia-Japan International Symposium on Advanced Technology 2007, 12-15th November 2007, Kuala Lumpur, Malaysia.
           Structure damage detection using neural network with multi-stage substructuring    
Bakhary, NorHisham and Hao, Hong and Deeks, Andrew J. (2010) Structure damage detection using neural network with multi-stage substructuring. Advances in Structural Engineering, 13 (1). pp. 1-16. ISSN 1369-4332
           Simulation and optimization of heavy oil cracking (HOC) unit using neural network and genetic algorithm    
Zahedi, Gholamreza and Abdul Mana, Zainuddin (2010) Simulation and optimization of heavy oil cracking (HOC) unit using neural network and genetic algorithm. Project Report. Sustainability, Skudai, Johor. (Unpublished)
           Utilization of RADARSAT-1 SAR data for modelling sea surface current using hopfield neural network    
Marghany, Maged and Hashim, Mazlan (2009) Utilization of RADARSAT-1 SAR data for modelling sea surface current using hopfield neural network. In: Proceeding of 2009 IEEE International Conference on Antennas, Propagation and Systems (INAS 2009), 2009.
           Hopfield neural network for sea surface current tracking from RADARSAT-1 SAR data    
Marghany, Maged and Hashim, Mazlan and Mansor, Shattri (2009) Hopfield neural network for sea surface current tracking from RADARSAT-1 SAR data. In: Joint 8th International Symposium and Exhibition on Geoinformation 2009 & ISPRS Symposium on Spatial Decision Support System and LBS 2009 , 2009, Crowne Plaza Mutiara Hotel, Kuala Lumpur .
           Development of a robust hybrid estimator using partial least squares regression and artificial neural networks.    
Ahmad, Arshad and Lim, Wan Piang (2003) Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. Proceedings of International Conference On Chemical and Bioprocess Engineering, 2 . pp. 780-787.
           Improving robustness of artificial neural networks model using genetic algorithm    
Ahmad, Arshad and Chen, Wah Sit (2003) Improving robustness of artificial neural networks model using genetic algorithm. Proceedings of International Conference On Chemical and Bioprocess Engineering, 2 . pp. 793-800.
           Application of neural network technique and electrodynamic sensors in the identification of solid flow regimes    
Rahmat, Mohd. Fua'ad and Sabit, Hakilo Amed (2007) Application of neural network technique and electrodynamic sensors in the identification of solid flow regimes. Jurnal Teknologi, 46 (D). pp. 77-92. ISSN 0127-9696
           Artificial neural network modelling for the prediction of carbon surface area    
Abd. Razak, Norhuda and Arshad, Khairil Anuar and Abd. Rahman, Ali and M. Sanip, Suhaila and Ismail, Ahmad Fauzi (2004) Artificial neural network modelling for the prediction of carbon surface area. In: The XXI Regional Conference on Solid State Science & Technology 2004, 12-13 October 2004, Hyatt Regency Kinabalu, Sabah, Malaysia.
           Hydrogen desorption study of as-synthesized carbon nanotubes using artificial neural network    
Abdul Rahman, Ali and Arshad, Khairil Anuar and Abd. Razak, Norhuda and M. Sanip, Suhaila and Ismail, Ahmad Fauzi (2005) Hydrogen desorption study of as-synthesized carbon nanotubes using artificial neural network. In: 3rd International Conference on Materials for Advanced Technologies (ICMAT 2005) and 9th International Conference On Advanced Materials (ICAM 2005), 3-8 July 2005, Singapore.
           Modeling of tapioca starch hydrolysis using neural networks    
Rashid, Roslina and Saidina Amin, Nor Aishah and Jamaluddin, Hishamuddin (2001) Modeling of tapioca starch hydrolysis using neural networks. Proceedings of The 15th Symposium of Malaysian Chemical Engineers SOMChE 2001 . pp. 461-465.
           Neural network inferential estimator for product composition of a fatty acid distillation column    
Ahmad, Arshad and Wong, Teck Siang (2001) Neural network inferential estimator for product composition of a fatty acid distillation column. Proceedings of The 15th Symposium of Malaysian Chemical Engineers SOMChE 2001 . pp. 191-196.
           Process identification using artificial neural network    
Ahmad, Arshad (1995) Process identification using artificial neural network. Proceedings of The Eleventh Symposium of Malaysia Chemical Engineers . C3-1.
          The application of deep convolutional neural networks to ultrasound for modelling of dynamic states within human skeletal muscle. (arXiv:1706.09450v1 [cs.CV])   

Authors: Ryan J. Cunningham, Peter J. Harding, Ian D. Loram

This paper concerns the fully automatic direct in vivo measurement of active and passive dynamic skeletal muscle states using ultrasound imaging. Despite the long standing medical need (myopathies, neuropathies, pain, injury, ageing), currently technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal intramuscular states. Ultrasound provides a technology in which static and dynamic muscle states can be observed non-invasively, yet current computational image understanding approaches are inadequate. We propose a new approach in which deep learning methods are used for understanding the content of ultrasound images of muscle in terms of its measured state. Ultrasound data synchronized with electromyography of the calf muscles, with measures of joint torque/angle were recorded from 19 healthy participants (6 female, ages: 30 +- 7.7). A segmentation algorithm previously developed by our group was applied to extract a region of interest of the medial gastrocnemius. Then a deep convolutional neural network was trained to predict the measured states (joint angle/torque, electromyography) directly from the segmented images. Results revealed for the first time that active and passive muscle states can be measured directly from standard b-mode ultrasound images, accurately predicting for a held out test participant changes in the joint angle, electromyography, and torque with as little error as 0.022{\deg}, 0.0001V, 0.256Nm (root mean square error) respectively.


          Toward Computation and Memory Efficient Neural Network Acoustic Models with Binary Weights and Activations. (arXiv:1706.09453v1 [cs.CL])   

Authors: Liang Lu

Neural network acoustic models have significantly advanced state of the art speech recognition over the past few years. However, they are usually computationally expensive due to the large number of matrix-vector multiplications and nonlinearity operations. Neural network models also require significant amounts of memory for inference because of the large model size. For these two reasons, it is challenging to deploy neural network based speech recognizers on resource-constrained platforms such as embedded devices. This paper investigates the use of binary weights and activations for computation and memory efficient neural network acoustic models. Compared to real-valued weight matrices, binary weights require much fewer bits for storage, thereby cutting down the memory footprint. Furthermore, with binary weights or activations, the matrix-vector multiplications are turned into addition and subtraction operations, which are computationally much faster and more energy efficient for hardware platforms. In this paper, we study the applications of binary weights and activations for neural network acoustic modeling, reporting encouraging results on the WSJ and AMI corpora.


          Real-time Distracted Driver Posture Classification. (arXiv:1706.09498v1 [cs.CV])   

Authors: Yehya Abouelnaga, Hesham M. Eraqi, Mohamed N. Moustafa

Distracted driving is a worldwide problem leading to an astoundingly increasing number of accidents and deaths. Existing work is concerned with a very small set of distractions (mostly, cell phone usage). Also, for the most part, it uses unreliable ad-hoc methods to detect those distractions. In this paper, we present the first publicly available dataset for "distracted driver" posture estimation with more distraction postures than existing alternatives. In addition, we propose a reliable system that achieves a 95.98% driving posture classification accuracy. The system consists of a genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show that a weighted ensemble of classifiers using a genetic algorithm yields in better classification confidence. We also study the effect of different visual elements (i.e. hands and face) in distraction detection by means of face and hand localizations. Finally, we present a thinned version of our ensemble that could achieve a 94.29% classification accuracy and operate in a real-time environment.


          Neural SLAM. (arXiv:1706.09520v1 [cs.LG])   

Authors: Jingwei Zhang, Lei Tai, Joschka Boedecker, Wolfram Burgard, Ming Liu

We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimicking that of traditional Simultaneous Localization and Mapping (SLAM) into the soft attention based addressing of external memory architectures, in which the external memory acts as an internal representation of the environment. This structure encourages the evolution of SLAM-like behaviors inside a completely differentiable deep neural network. We show that this approach can help reinforcement learning agents to successfully explore new environments where long-term memory is essential. We validate our approach in both challenging grid-world environments and preliminary Gazebo experiments.


          Learning to Learn: Meta-Critic Networks for Sample Efficient Learning. (arXiv:1706.09529v1 [cs.LG])   

Authors: Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, Yongxin Yang

We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can be applied to both reinforcement and supervised learning. The key idea is to learn a meta-critic: an action-value function neural network that learns to criticise any actor trying to solve any specified task. For supervised learning, this corresponds to the novel idea of a trainable task-parametrised loss generator. This meta-critic approach provides a route to knowledge transfer that can flexibly deal with few-shot and semi-supervised conditions for both reinforcement and supervised learning. Promising results are shown on both reinforcement and supervised learning problems.


          Transforming Musical Signals through a Genre Classifying Convolutional Neural Network. (arXiv:1706.09553v1 [cs.SD])   

Authors: S. Geng, G. Ren, M. Ogihara

Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.


          Music Signal Processing Using Vector Product Neural Networks. (arXiv:1706.09555v1 [cs.SD])   

Authors: Z.C. Fan, T.S. Chan, Y.H. Yang, J.S. R. Jang

We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.


          Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets. (arXiv:1706.09556v1 [cs.NE])   

Authors: A. Bazzica, J.C. van Gemert, C.C.S. Liem, A. Hanjalic

Acoustic events often have a visual counterpart. Knowledge of visual information can aid the understanding of complex auditory scenes, even when only a stereo mixdown is available in the audio domain, \eg identifying which musicians are playing in large musical ensembles. In this paper, we consider a vision-based approach to note onset detection. As a case study we focus on challenging, real-world clarinetist videos and carry out preliminary experiments on a 3D convolutional neural network based on multiple streams and purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5 hours of clarinetist videos together with cleaned annotations which include about 36,000 onsets and the coordinates for a number of salient points and regions of interest. By performing several training trials on our dataset, we learned that the problem is challenging. We found that the CNN model is highly sensitive to the optimization algorithm and hyper-parameters, and that treating the problem as binary classification may prevent the joint optimization of precision and recall. To encourage further research, we publicly share our dataset, annotations and all models and detail which issues we came across during our preliminary experiments.


          Talking Drums: Generating drum grooves with neural networks. (arXiv:1706.09558v1 [cs.SD])   

Authors: P. Hutchings

Presented is a method of generating a full drum kit part for a provided kick-drum sequence. A sequence to sequence neural network model used in natural language translation was adopted to encode multiple musical styles and an online survey was developed to test different techniques for sampling the output of the softmax function. The strongest results were found using a sampling technique that drew from the three most probable outputs at each subdivision of the drum pattern but the consistency of output was found to be heavily dependent on style.


          Audio Spectrogram Representations for Processing with Convolutional Neural Networks. (arXiv:1706.09559v1 [cs.SD])   

Authors: L. Wyse

One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.


          Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition. (arXiv:1706.09569v1 [cs.CL])   

Authors: Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi

Background. Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". Objectives. (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Methods. Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. Results. We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DDI-DrugBank and DDI-MedLine, but not in the 2010 i2b2/VA IRB Revision dataset. Conclusion. We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary.


          Multi-scale Multi-band DenseNets for Audio Source Separation. (arXiv:1706.09588v1 [cs.SD])   

Authors: Naoya Takahashi, Yuki Mitsufuji

This paper deals with the problem of audio source separation. To handle the complex and ill-posed nature of the problems of audio source separation, the current state-of-the-art approaches employ deep neural networks to obtain instrumental spectra from a mixture. In this study, we propose a novel network architecture that extends the recently developed densely connected convolutional network (DenseNet), which has shown excellent results on image classification tasks. To deal with the specific problem of audio source separation, an up-sampling layer, block skip connection and band-dedicated dense blocks are incorporated on top of DenseNet. The proposed approach takes advantage of long contextual information and outperforms state-of-the-art results on SiSEC 2016 competition by a large margin in terms of signal-to-distortion ratio. Moreover, the proposed architecture requires significantly fewer parameters and considerably less training time compared with other methods.


          CS591 Report: Application of siamesa network in 2D transformation. (arXiv:1706.09598v1 [cs.CV])   

Authors: Dorothy Chang

Deep learning has been extensively used various aspects of computer vision area. Deep learning separate itself from traditional neural network by having a much deeper and complicated network layers in its network structures. Traditionally, deep neural network is abundantly used in computer vision tasks including classification and detection and has achieve remarkable success and set up a new state of the art results in these fields. Instead of using neural network for vision recognition and detection. I will show the ability of neural network to do image registration, synthesis of images and image retrieval in this report.


          Deep learning bank distress from news and numerical financial data. (arXiv:1706.09627v1 [stat.ML])   

Authors: Paola Cerchiello, Giancarlo Nicola, Samuel Ronnqvist, Peter Sarlin

In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and symbolic text input onto a reduced latent semantic space. Afterwards, a second supervised neural network is trained combining news data with standard financial figures to classify banks whether in distressed or tranquil states, based on a small set of known distress events. Then the final aim is not only the improvement of the predictive performance of the classifier but also to assess the importance of news data in the classification process. Does news data really bring more useful information not contained in standard financial variables? Our results seem to confirm such hypothesis.


          Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. (arXiv:1706.09634v1 [cs.CV])   

Authors: Waleed M. Gondal, Jan M. Köhler, René Grzeszick, Gernot A. Fink, Michael Hirsch

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.


          Machine Learning Approaches to Energy Consumption Forecasting in Households. (arXiv:1706.09648v1 [cs.NE])   

Authors: Riccardo Bonetto, Michele Rossi

We consider the problem of power demand forecasting in residential micro-grids. Several approaches using ARMA models, support vector machines, and recurrent neural networks that perform one-step ahead predictions have been proposed in the literature. Here, we extend them to perform multi-step ahead forecasting and we compare their performance. Toward this end, we implement a parallel and efficient training framework, using power demand traces from real deployments to gauge the accuracy of the considered techniques. Our results indicate that machine learning schemes achieve smaller prediction errors in the mean and the variance with respect to ARMA, but there is no clear algorithm of choice among them. Pros and cons of these approaches are discussed and the solution of choice is found to depend on the specific use case requirements. A hybrid approach, that is driven by the prediction interval, the target error, and its uncertainty, is then recommended.


          Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines. (arXiv:1706.09667v1 [cs.IT])   

Authors: Maxinder S. Kanwal, Joshua A. Grochow, Nihat Ay

In the past three decades, many theoretical measures of complexity have been proposed to help understand complex systems. In this work, for the first time, we place these measures on a level playing field, to explore the qualitative similarities and differences between them, and their shortcomings. Specifically, using the Boltzmann machine architecture (a fully connected recurrent neural network) with uniformly distributed weights as our model of study, we numerically measure how complexity changes as a function of network dynamics and network parameters. We apply an extension of one such information-theoretic measure of complexity to understand incremental Hebbian learning in Hopfield networks, a fully recurrent architecture model of autoassociative memory. In the course of Hebbian learning, the total information flow reflects a natural upward trend in complexity as the network attempts to learn more and more patterns.


          Image classification using local tensor singular value decompositions. (arXiv:1706.09693v1 [stat.ML])   

Authors: Elizabeth Newman, Misha Kilmer, Lior Horesh

From linear classifiers to neural networks, image classification has been a widely explored topic in mathematics, and many algorithms have proven to be effective classifiers. However, the most accurate classifiers typically have significantly high storage costs, or require complicated procedures that may be computationally expensive. We present a novel (nonlinear) classification approach using truncation of local tensor singular value decompositions (tSVD) that robustly offers accurate results, while maintaining manageable storage costs. Our approach takes advantage of the optimality of the representation under the tensor algebra described to determine to which class an image belongs. We extend our approach to a method that can determine specific pairwise match scores, which could be useful in, for example, object recognition problems where pose/position are different. We demonstrate the promise of our new techniques on the MNIST data set.


          AP17-OLR Challenge: Data, Plan, and Baseline. (arXiv:1706.09742v1 [cs.CL])   

Authors: Zhiyuan Tang, Dong Wang, Yixiang Chen, Qing Chen

We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR. Compared to the event last year (AP16-OLR), the new challenge involves more languages and focuses more on short utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two types of baselines are constructed to assist the participants, one is based on the i-vector model and the other is based on various neural networks. We report the baseline results evaluated with various metrics defined by the AP17-OLR evaluation plan and demonstrate that the combined database is a reasonable data resource for multilingual research. All the data is free for participants, and the Kaldi recipes for the baselines have been published online.


           Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network    
Saidina Amin, Nor Aishah and Mohd. Yusof, Khairiyah and Isha, Ruzinah (2005) Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network. Jurnal Teknologi F (43F). pp. 15-30. ISSN 0127-9696
          Scale-Aware Face Detection. (arXiv:1706.09876v1 [cs.CV])   

Authors: Zekun Hao, Yu Liu, Hongwei Qin, Junjie Yan, Xiu Li, Xiaolin Hu

Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. They rely on either fitting a large single model to faces across a large scale range or multi-scale testing. Both are computationally expensive. We propose Scale-aware Face Detector (SAFD) to handle scale explicitly using CNN, and achieve better performance with less computation cost. Prior to detection, an efficient CNN predicts the scale distribution histogram of the faces. Then the scale histogram guides the zoom-in and zoom-out of the image. Since the faces will be approximately in uniform scale after zoom, they can be detected accurately even with much smaller CNN. Actually, more than 99% of the faces in AFW can be covered with less than two zooms per image. Extensive experiments on FDDB, MALF and AFW show advantages of SAFD.


          Towards Understanding the Dynamics of Generative Adversarial Networks. (arXiv:1706.09884v1 [cs.LG])   

Authors: Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

Generative Adversarial Networks (GANs) have recently been proposed as a promising avenue towards learning generative models with deep neural networks. While GANs have demonstrated state-of-the-art performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory and in practice. To address this issue, we take a first step towards a rigorous study of GAN dynamics. We propose a simple model that exhibits several of the common problematic convergence behaviors (e.g., vanishing gradient, mode collapse, diverging or oscillatory behavior) and still allows us to establish the first convergence bounds for parametric GAN dynamics. We find an interesting dichotomy: a GAN with an optimal discriminator provably converges, while a first order approximation of the discriminator leads to unstable GAN dynamics and mode collapse. Our model and analysis point to a specific challenge in practical GAN training that we call discriminator collapse.


          Automatic Face Image Quality Prediction. (arXiv:1706.09887v1 [cs.CV])   

Authors: Lacey Best-Rowden, Anil K. Jain

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcher-independent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.


          An Analysis of Ability in Deep Neural Networks. (arXiv:1702.04811v2 [cs.CL] UPDATED)   

Authors: John P. Lalor, Hao Wu, Tsendsuren Munkhdalai, Hong Yu

Deep neural networks (DNNs) have made significant progress in a number of Machine Learning applications. However without a consistent set of evaluation tasks, interpreting performance across test datasets is impossible. In most previous work, characteristics of individual data points are not considered during evaluation, and each data point is treated equally. Using Item Response Theory (IRT) from psychometrics it is possible to model characteristics of specific data points that then inform an estimate of model ability as compared to a population of humans. We report the results of several experiments to determine how different Deep Neural Network (DNN) models perform under different training circumstances with respect to ability. As DNNs train on larger datasets, performance begins to look like human performance under the assumptions of IRT models. That is, easy questions start to have a higher probability of being answered correctly than harder questions. We also report the results of additional analyses regarding model robustness to noise and performance as a function of training set size that further inform our main conclusion


          A Random Matrix Approach to Neural Networks. (arXiv:1702.05419v2 [math.PR] UPDATED)   

Authors: Cosme Louart, Zhenyu Liao, Romain Couillet

This article studies the Gram random matrix model $G=\frac1T\Sigma^{\rm T}\Sigma$, $\Sigma=\sigma(WX)$, classically found in the analysis of random feature maps and random neural networks, where $X=[x_1,\ldots,x_T]\in{\mathbb R}^{p\times T}$ is a (data) matrix of bounded norm, $W\in{\mathbb R}^{n\times p}$ is a matrix of independent zero-mean unit variance entries, and $\sigma:{\mathbb R}\to{\mathbb R}$ is a Lipschitz continuous (activation) function --- $\sigma(WX)$ being understood entry-wise. By means of a key concentration of measure lemma arising from non-asymptotic random matrix arguments, we prove that, as $n,p,T$ grow large at the same rate, the resolvent $Q=(G+\gamma I_T)^{-1}$, for $\gamma>0$, has a similar behavior as that met in sample covariance matrix models, involving notably the moment $\Phi=\frac{T}n{\mathbb E}[G]$, which provides in passing a deterministic equivalent for the empirical spectral measure of $G$. Application-wise, this result enables the estimation of the asymptotic performance of single-layer random neural networks. This in turn provides practical insights into the underlying mechanisms into play in random neural networks, entailing several unexpected consequences, as well as a fast practical means to tune the network hyperparameters.


          CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability. (arXiv:1702.08563v2 [cs.CL] UPDATED)   

Authors: John P. Lalor, Hao Wu, Hong Yu

Item Response Theory (IRT) allows for measuring ability of Machine Learning models as compared to a human population. However, it is difficult to create a large dataset to train the ability of deep neural network models (DNNs). We propose Crowd-Informed Fine-Tuning (CIFT) as a new training process, where a pre-trained model is fine-tuned with a specialized supplemental training set obtained via IRT model-fitting on a large set of crowdsourced response patterns. With CIFT we can leverage the specialized set of data obtained through IRT to inform parameter tuning in DNNs. We experiment with two loss functions in CIFT to represent (i) memorization of fine-tuning items and (ii) learning a probability distribution over potential labels that is similar to the crowdsourced distribution over labels to simulate crowd knowledge. Our results show that CIFT improves ability for a state-of-the art DNN model for Recognizing Textual Entailment (RTE) tasks and is generalizable to a large-scale RTE test set.


          Hardware Automated Dataflow Deployment of CNNs. (arXiv:1705.04543v3 [cs.OH] UPDATED)   

Authors: Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot, Cedric Bourrasset, Jean-Charles Quinton, François Berry

Deep Convolutional Neural Networks (CNNs) are the state of the art systems for image classification and scene understating. However, such techniques are computationally intensive and involve highly regular parallel computation. CNNs can thus benefit from a significant acceleration in execution time when running on fine grain programmable logic devices. As a consequence, several studies have proposed FPGA-based accelerators for CNNs. However, because of the huge amount of the required hardware resources, none of these studies directly was based on a direct mapping of the CNN computing elements onto the FPGA physical resources. In this work, we demonstrate the feasibility of this so-called direct hardware mapping approach and discuss several associated implementation issues. As a proof of concept, we introduce the haddoc2 open source tool, that is able to automatically transform a CNN description into a platform independent hardware description for FPGA implementation.


          Unsupervised Person Re-identification: Clustering and Fine-tuning. (arXiv:1705.10444v2 [cs.CV] UPDATED)   

Authors: Hehe Fan, Liang Zheng, Yi Yang

The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re-identification (re-ID). In this paper, we consider the more pragmatic issue of learning a deep feature with no or only a few labels. We propose a progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains. Our method is easy to implement and can be viewed as an effective baseline for unsupervised re-ID feature learning. Specifically, PUL iterates between 1) pedestrian clustering and 2) fine-tuning of the convolutional neural network (CNN) to improve the original model trained on the irrelevant labeled dataset. Since the clustering results can be very noisy, we add a selection operation between the clustering and fine-tuning. At the beginning when the model is weak, CNN is fine-tuned on a small amount of reliable examples which locate near to cluster centroids in the feature space. As the model becomes stronger in subsequent iterations, more images are being adaptively selected as CNN training samples. Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence. This process is naturally formulated as self-paced learning. We then point out promising directions that may lead to further improvement. Extensive experiments on three large-scale re-ID datasets demonstrate that PUL outputs discriminative features that improve the re-ID accuracy.


          An End-to-End Computer Vision Pipeline for Automated Cardiac Function Assessment by Echocardiography. (arXiv:1706.07342v2 [cs.CV] UPDATED)   

Authors: Rahul C. Deo, Jeffrey Zhang, Laura A. Hallock, Sravani Gajjala, Lauren Nelson, Eugene Fan, Mandar A. Aras, ChaRandle Jordan, Kirsten E. Fleischmann, Michelle Melisko, Atif Qasim, Sanjiv J. Shah, Ruzena Bajcsy

Background: Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways including enabling low-cost assessment of cardiac function in the primary care setting. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram (echo) interpretation, with a focus on evaluation of left ventricular function. Methods: Our approach entailed: 1) preprocessing, which includes auto-downloading of echo studies, metadata extraction, de-identification, and conversion of images into numerical arrays; 2) convolutional neural networks (CNN) for view identification; 3) localization of the left ventricle and delineation of cardiac boundaries using active appearance models (AAM); 4) identification of properly segmented images using gradient boosting; and 5) particle tracking to compute longitudinal strain. Results: CNNs identified views with high accuracy (e.g. 95% for apical 4-chamber) and the combination of CNN/bounding box determination/AAM effectively segmented 67-88% of videos. We analyzed 2775 apical videos from patients with heart failure and found good concordance with vendor-derived longitudinal strain measurements, both at the individual video level (r=0.77) and at the patient level (r=0.51). We also analyzed 9402 videos from breast cancer patients undergoing serial monitoring for trastuzumab cardiotoxicity to illustrate the potential for automated, quality-weighted modeling of patient trajectories. Conclusions: We demonstrate the feasibility of a fully automated echocardiography analysis pipeline for assessment of left ventricular function. Our work lays the groundwork for using automated interpretation to support point-of-care handheld cardiac ultrasound and may enable large-scale analysis of the millions of echos currently archived within healthcare systems.


          (USA-FL-Tampa) Senior Modeling Analyst   
Purpose of Job IMPORTANT: Applicants – When filling out your name and other personal information below, DO NOT USE ALL CAPS or any special characters. Use only standard letters in the English alphabet. Including special characters or all uppercase letters will cause errors in your application. We are currently seeking talented Senior Modeling Analyst (AML) for our Phoenix, AZ or San Antonio, TX facility. The ideal candidate for this position has experience creating, tuning and monitoring models designed to monitor transactions and Anti-Money Laundering customer risk within mid to large size financial institutions. This position requires performing duties within a highly regulated anti-money laundering or financial crimes team and within the financial institutions model governance policy. Teamwork and communication with business partners are essential to team success. Develop and analyze data to predict business results or member behavior. Expert knowledge in statistics, mathematics, and tools used in predictive modeling. Partner cross-functionally with business to deliver breakthrough analytical solutions to support a winning strategy in a continually changing business environment. Job Requirements * Lead the development, enhancement and implementation of statistical and other quantitative models to support forecasting, member behavior-based scoring and other business applications. * Understand technical issues in econometric and statistical modeling and apply these skills toward solving business problems. Identify opportunities to apply quantitative methods to improve business performance. * Full ownership of the model development process and relationship with the business customer: from conceptualization through data exploration, model selection and validation, implementation, business user training and support. * Strong understanding of the model lifecycle management process with ability to identify gaps and opportunities for improvement in business applications. * Develop model monitoring plan, monitor statistical model performance, and provide technical guidance to business leadership. * Communicate technical subject matter clearly and concisely to individuals from various backgrounds. * Communicate technical subject matter clearly and concisely to individuals from various backgrounds. *Minimum Requirements* * 6+ years related work experience in statistical analysis and model development Or If a Master's Degree, 4+ years related experience in statistical model development Or If a Ph.D.,3+ years related experience in statistical model development * Advanced knowledge of data analysis tools and industry data sources * Expert knowledge in developing analysis queries and procedures in SQL, SAS, BI tools or other analysis software * Expert knowledge in several statistical techniques (Generalized linear modeling, Time Series, CART, Decision Trees, Neural Networks, Factor analysis experimental design, hypothesis testing, and/or advance techniques). * Bachelor's degree in Statistics, Economics, Engineering, Mathematics, Actuarial Sciences or related field OR A Master's Degree in Statistics, Economics, Engineering, Mathematics, Actuarial Sciences or related field OR A PhD in Statistics, Economics, Engineering, Mathematics, Actuarial Sciences or related field. *Preferred* * Direct modeling experience related to Anti-Money Laundering or Financial Crimes typologies * Experience managing model throughout the model’s lifecycle within a financial institutions model governance policy * Experience with models related to Actimize or comparable vendor risk monitoring tools * Experience in categorical data analysis * Direct experience analyzing and identifying patterns, trends, and insights within data * Experience in analyzing customer, transactional, and financial product data in databases such as Oracle, SQL Server * Experience partnering with IT to deploy results of analysis into production *Knowledge/Skills/Attributes* Business Acumen; Collaboration (Team Building); Communication; Demonstrate Adaptability (Agility); Drive for Results; Innovation. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. At USAA our employees enjoy one of the best benefits packages in the business, including a flexible business casual or casual dress environment, comprehensive medical, dental and vision plans, along with wellness and wealth building programs. Additionally, our career path planning and continuing education will assist you with your professional goals. *Relocation* assistance is *not* *available* for this position. *Senior Modeling Analyst* *FL-Tampa* *R0010015*
          (USA-FL-Tampa) Lead Modeling Analyst   
Purpose of Job IMPORTANT: External Applicants – When filling out your name and other personal information below, DO NOT USE ALL CAPS or any special characters. Use only standard letters in the English alphabet. Including special characters or all uppercase letters will cause errors in your application. We are currently seeking talented Lead Modeling Analyst (AML) for our Phoenix, AZ or San Antonio, TX facility. The ideal candidate for this position has experience creating, tuning and monitoring models designed to monitor transactions and Anti-Money Laundering customer risk within mid to large size financial institutions. This position requires performing duties within a highly regulated anti-money laundering or financial crimes team and within the financial institutions model governance policy. Teamwork and communication with business partners are essential to team success. Develop and analyze data to predict business results or member behavior. Expert knowledge in statistics, mathematics, and tools used in predictive modeling. Partner cross-functionally with business to deliver breakthrough analytical solutions to support a winning strategy in a continually changing business environment. Job Requirements * Lead the development, enhancement and implementation of statistical and other quantitative models to support forecasting, member behavior-based scoring and other business applications. * Understand technical issues in econometric and statistical modeling and apply these skills toward solving business problems. Identify opportunities to apply quantitative methods to improve business performance. * Full ownership of the model development process and relationship with the business customer: from conceptualization through data exploration, model selection and validation, implementation, business user training and support. * Strong understanding of the model lifecycle management process with ability to identify gaps and opportunities for improvement in business applications. * Develop model monitoring plan, monitor statistical model performance, and provide technical guidance to business leadership. * Communicate technical subject matter clearly and concisely to individuals from various backgrounds. * Communicate technical subject matter clearly and concisely to individuals from various backgrounds. *Minimum Requirements* * 6+ years related work experience in statistical analysis and model development Or If a Master's Degree, 4+ years related experience in statistical model development Or If a Ph.D.,3+ years related experience in statistical model development * Advanced knowledge of data analysis tools and industry data sources * Expert knowledge in developing analysis queries and procedures in SQL, SAS, BI tools or other analysis software * Expert knowledge in several statistical techniques (Generalized linear modeling, Time Series, CART, Decision Trees, Neural Networks, Factor analysis experimental design, hypothesis testing, and/or advance techniques). * Bachelor's degree in Statistics, Economics, Engineering, Mathematics, Actuarial Sciences or related field OR A Master's Degree in Statistics, Economics, Engineering, Mathematics, Actuarial Sciences or related field OR A PhD in Statistics, Economics, Engineering, Mathematics, Actuarial Sciences or related field. *Qualifications may warrant placement in a different job level.* When you apply for this position, you will be required to answer some initial questions. This will take approximately 5 minutes. Once you begin the questions you will not be able to finish them at a later time and you will not be able to change your responses. *Preferred* * 2 or more years of modeling experience related to Anti-Money Laundering or Financial Crimes typologies * Deep experience managing model throughout the model’s lifecycle within a financial institutions model governance policy * Experience with models related to Actimize or comparable vendor risk monitoring tools * Direct Experience reviewing and validating the model development work of others * Experience presenting model methodologies to Executives, Auditors and Examiners * Experience in developing insights from data, and building dashboards for business to consume * Direct experience reviewing and validating statistical analysis performed by others to establish scenario or rules parameters. Communicate the implications of scenario parameter choices to the appropriate stakeholders * Experience in categorical data analysis * Experience in analyzing customer, transactional, and financial product data in databases such as Oracle, * SQL Server * Experience partnering with IT to deploy results of analysis into production *Knowledge/Skills/Attributes* Business Acumen; Collaboration (Team Building); Communication; Demonstrate Adaptability (Agility); Drive for Results; Innovation. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. At USAA our employees enjoy one of the best benefits packages in the business, including a flexible business casual or casual dress environment, comprehensive medical, dental and vision plans, along with wellness and wealth building programs. Additionally, our career path planning and continuing education will assist you with your professional goals. *Relocation* assistance is *not* *available* for this position. *For Internal Candidates:* Must complete 12 months in current position (from date of hire or date of placement), or must have manager’s approval prior to posting. *Last day for internal candidates* *to apply to the opening is 5/03/17 by 11:59 pm CST time.* *Lead Modeling Analyst* *FL-Tampa* *R0010014*
          Artificial intelligence – it’s deep (learning)    
Maschinenmensch (machine-human) on display at the Science Museum at Preview Of The Science Museum's Robots Exhibition

Maschinenmensch (machine-human) on display at the Science Museum at Preview Of The Science Museum's Robots Exhibition at Science Museum London.; Credit: Ming Yeung/Getty Images Entertainment Video

AirTalk®

To what extent can we trust artificial intelligence if we don’t understand its decision-making process?

This may sound like a science fiction scenario, but it’s an ethical dilemma that we’re already grappling with.

In his recent MIT Technology Review cover story, “The Dark Secret at the Heart of AI,” Will Knight explores the ethical problems presented by deep learning.  

Some context: one of the most efficient types of artificial intelligence is machine learning – that’s when you program a computer to write its own algorithms. Deep learning is a subset of machine learning which involves training a neural network, a mathematical approximation of the way neurons process information, often by feeding it examples and allowing it to “learn.”

This technique has taken the tech world by storm, and is already being used for language translation, image captioning and translation. The possibilities are extensive, with powerful decision making potential that can be used for self-driving cars, the military and medicine.

However, when an AI writes its own algorithm, it often becomes so complicated that a human can’t decipher it, creating a “black box,” and an ethical dilemma. What are the trade-offs to using this powerful technique? To what extent can humans trust a decision-making process that they can’t understand? How can we regulate it?

Guest host Libby Denkmann in for Larry Mantle

Guest:

Will Knight, senior editor for AI at MIT Technology Review; he wrote the article “The Dark Secret at the Heart of AI;” he tweets @willknight

This content is from Southern California Public Radio. View the original story at SCPR.org.


          Artificial Synapses Could Lead to Smarter AI   
By replicating the function of the human brain's 100 trillion synapses, scientists hope to boost the versatility of artificial neural networks.
          Peering into neural networks   
Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for
           انجام پايان نامه کارشناسي ارشدحقوق    
انجام پايان نامه ارشد ودکتري حقوق وکامپيوتر انجام پروژه هاي دانشجويي براي دانشجويان ايراني داخل و خارج ازکشوررشته کامپيوتروحقوق Several suggested student programming projects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكتري و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات وحقوق خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد درمورد نقش erp و سيستم هاي اطلاعاتي و ريسک در هوش تجاري وبررسي الگوريتمهاي شبکهاي گيريد* داده کاوي (Data Mrining) در زمينه هاي دسته بندي (Classification)، خوشه بندي (Clustering)، پيش بيني (Prediction)، انتخاب ويژگي (Feature Selection) و قواعد انجمني (Association Rules) با *وب سرويس و.... الگوريتم lulea*سيستم هاي چندعامله @انجام پروژه هاي پردازش تصوير فازي* الگوريتم ژنتيك* شبكه عصبي *هوش مصنوعي * شبيه سازي *بهينه سازي *سمينار*–الگوريتم چندهدفه* تكاملي *سيمولينک*بينايي ماشين*فازي کامينز*. Image Processing amp; Machine vision* SIMULINK, cloud storager و IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK* و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhanceme انجام مقاله، پروپوزال و انجام پايان نامه در تمامي گرايش هاي رشته حقوق انتخاب و پيشنهاد موضوع پايان نامه و انجام پروپوزال کارشناسي ارشد و دکتري در تمامي گرايش هاي حقوق: حقوق خصوصي فقه و حقوق خصوصي حقوق جزا و جرم شناسي فقه و حقوق جزا حقوق تجار حقوق ثبت اسناد رسم حقوق بين‌المل حقوق اقتصاد حقوق بشر حقوق تجارت بين‌الملل حقوق محيط زيست حقوق عمومي فقه و حقوق اسلامي فقه و مباني حقوق اسلامي فقه و مباني حقوق و انديشه ي امام خميني جهت سفارش پروژه تماس ب*****ريد 09191022908 www.tezcomputer.com www.pcporoje.com
           انجام پايان نامه کارشناسي ارشدحقوق جزاوجرم شناسي    
انجام پايان نامه ارشد ودکتري حقوق وکامپيوتر انجام پروژه هاي دانشجويي براي دانشجويان ايراني داخل و خارج ازکشوررشته کامپيوتروحقوق Several suggested student programming projects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكتري و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات وحقوق خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد درمورد نقش erp و سيستم هاي اطلاعاتي و ريسک در هوش تجاري وبررسي الگوريتمهاي شبکهاي گيريد* داده کاوي (Data Mrining) در زمينه هاي دسته بندي (Classification)، خوشه بندي (Clustering)، پيش بيني (Prediction)، انتخاب ويژگي (Feature Selection) و قواعد انجمني (Association Rules) با *وب سرويس و.... الگوريتم lulea*سيستم هاي چندعامله @انجام پروژه هاي پردازش تصوير فازي* الگوريتم ژنتيك* شبكه عصبي *هوش مصنوعي * شبيه سازي *بهينه سازي *سمينار*–الگوريتم چندهدفه* تكاملي *سيمولينک*بينايي ماشين*فازي کامينز*. Image Processing amp; Machine vision* SIMULINK, cloud storager و IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK* و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhanceme انجام مقاله، پروپوزال و انجام پايان نامه در تمامي گرايش هاي رشته حقوق انتخاب و پيشنهاد موضوع پايان نامه و انجام پروپوزال کارشناسي ارشد و دکتري در تمامي گرايش هاي حقوق: حقوق خصوصي فقه و حقوق خصوصي حقوق جزا و جرم شناسي فقه و حقوق جزا حقوق تجار حقوق ثبت اسناد رسم حقوق بين‌المل حقوق اقتصاد حقوق بشر حقوق تجارت بين‌الملل حقوق محيط زيست حقوق عمومي فقه و حقوق اسلامي فقه و مباني حقوق اسلامي فقه و مباني حقوق و انديشه ي امام خميني جهت سفارش پروژه تماس ب*****ريد 09191022908 www.tezcomputer.com www.pcporoje.com
           مشاوره رايگان انتخاب موضوع پايان نامه کارشناسي ارشد    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات و.. خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامAي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين دانلودرايگان پروژه هاي دانشجويي دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب سيستم شهرت پيشنهاد ، تجزيه و تحليل اسپم ، ، تجزيه و تحليل اسکايپ فروشگاه مجازي تحت وب (فروشگاه اينترنتي) ، سايت آگهي ها سيستم کتابخانه ، سيستم هتل ، سيستم رستوران سيستم بيمارستان ، سيستم مطب و کلينيک ، سيستم داروخانه ‏* مجموعه کامل سورس هاي ‏API‏ توابع ‏‏(2000 سورس)‏*شبيه سازي ns2 ‏* سيستم تبديل رزومه افراد به فايل html شبيه سازي سيستم عابر بانک‏ ‏* تبديل تاريخ ( شمسي به ميلادي) ‏* ساعت آنالوگ و ديجيتال (3 نوع) ‏* شبيه سازي Paint ‏* شبيه سازي بازي تنيس تمام بازي هاي هوش مصنوعي دوزو*8پازل *8وزير*هواپيما*موش وگربه و..... *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايل عنوان* امضاي ديجيتال* *امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي * وب معنايي*آنتولوژي * فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 infoporoje.net@gmail.com
           انجام پروپوزال ارشد دكتري كامپيوتر IT    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات و.. خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامAي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين دانلودرايگان پروژه هاي دانشجويي دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب سيستم شهرت پيشنهاد ، تجزيه و تحليل اسپم ، ، تجزيه و تحليل اسکايپ فروشگاه مجازي تحت وب (فروشگاه اينترنتي) ، سايت آگهي ها سيستم کتابخانه ، سيستم هتل ، سيستم رستوران سيستم بيمارستان ، سيستم مطب و کلينيک ، سيستم داروخانه ‏* مجموعه کامل سورس هاي ‏API‏ توابع ‏‏(2000 سورس)‏*شبيه سازي ns2 ‏* سيستم تبديل رزومه افراد به فايل html شبيه سازي سيستم عابر بانک‏ ‏* تبديل تاريخ ( شمسي به ميلادي) ‏* ساعت آنالوگ و ديجيتال (3 نوع) ‏* شبيه سازي Paint ‏* شبيه سازي بازي تنيس تمام بازي هاي هوش مصنوعي دوزو*8پازل *8وزير*هواپيما*موش وگربه و..... *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايل عنوان* امضاي ديجيتال* *امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي * وب معنايي*آنتولوژي * فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 infoporoje.net@gmail.com
           پروژه پردازش تصوير با متلب،داده کاوي،شبکه هاي    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات و.. خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد SIMULINK, cloud storager و IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK* و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : ==================================== * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,… IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عامل پيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: ==================================== VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
           موضوعات پايان نامه کارشناسي ارشد هوش مصنوعي،نرم افزار،شبکه    
انجام کليه پروژه هاي دانشجوييدرسراسرايران بيش از 20 پروژه برنامه نويسيوپايان نامه پروپوزال هاي دانشجويي از دپارتمان علوم رايانه دانشگاه هاي کلمبيا هندما*****ي آلمان*سوئد*دانمارک *انگلستان *فيليپين *دبي*ترکيه و... دربانک پروژه پايتخت توسط خودگروه نرم افزاري پايتخت انجام پروژه هاي دانشجويي براي دانشجويانايراني داخل وخارجازکشوررشته کامپيوتر Several suggested student programming projects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه وپروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و....دانشگاه هاي داخل و خارج از کشوررشته کامپيوترنرم افزار*معماري کامپيوتر*هوش مصنوعي و فناوري اطلاعات و.........امنيت شبکه* مخابرات امن *تجارت الکترونيک تحت تمامي زبانها برنامه نويسي خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوقالذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبنديتوافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشيبراي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد درمورد نقش erp و سيستم هاي اطلاعاتي و ريسک در هوش تجاري بررسي انواع چالش‌هاي موجود در رايانش ابري و رايانش توري(Cloud computing amp; Grid computing) شامل مباحث امنيت (Security)، ذخيره‌سازي (Storage)، کارايي (Performance)، دسترس‌پذيري (Availability) و مديريت، تخصيص و زمانبندي منابع (Allocation and Scheduling Resources)، توازن بار(Load Balancing). بررسي انواع الگوريتم‌ها در حوزه‌ي داده‌کاوي (Data Mining)؛ طبقه‌بندي(Classification)، خوشه‌بندي(Clustering)، کشف قوانين انجمني(Association Rules)، پيش‌بيني سري‌زماني(Time Series Prediction)، انتخاب ويژ***** (Feature Selection) و استخراج ويژ***** (Feature Extraction)، کاهش بعد(Dimensionality Reduction)، شخصي سازي نتايج موتورهاي جستجو و داده‌کاوي اطلاعات آنها(Search Engine). بررسي انواع الگوريتم‌ها در حوزه‌ي شبکه‌هاي اجتماعي(Social Network)؛ کشف ساختار(structure Detection ) کشف اجتماعات(Community Detection)، تشخيص اسپم(Spam Filter). بررسي انواع تکنولوژي‌هاي ذخيره داده اي، Sql، NoSql، نگاشت کاهش (MapReduce)، هادوپ(Hadoop)، کار با Big Data. بررسي، مقايسه و بهبود انواع الگوريتم‌هاي مکاشفه‌اي، فرا مکاشفه‌اي و چند هدفه مانند الگوريتم ژنتيک(Genetic Algorithm, MOGA, NSGAII)، الگوريتم ازدحام ذرات(PSO, MOPSO)، الگوريتم مورچگان(Ant Colony)، الگوريتم زنبور عسل(Bee clolony)، الگوريتم رقابت استعماري(ICA)، الگوريتم فرهن***** (Cultural Algorithm)، الگوريتم تکامل تفاضلي(DE). بررسي انواع الگوريتم‌هاي پردازش تصوير(IMAGE PROCESSING)؛ تشخيص چهره(Face Recognation)، قطعه‌بندي تصاوير(Image Segmentation)، فشرده‌سازي تصاوير(Image Compression)، نهان‌نگاري تصاوير(Watermarking). بررسي انواع الگوريتم‌هاي ياد*****ر؛ شبکه‌هاي عصبي (ANFIS, ANN)، شبکه‌هاي بيزين(Bayesian Network)، ماشين بردار پشتيبان(SVM). استفاده از نرم‌افزار‌هاي Visual Studio، متلب(Matlab)، وکا(Weka)، رپيدماينر(Rapidminer)، Clementine، کلودسيم(Cloudsim). استفاده از زبان‌هاي Python, Java, C, C#, C++, DBMS, MySql, Sql Server, VB.NET, PHP تدوين پروپوزال، اجراي پايان نامه و طرح هاي پژوهشي و … وبررسي الگوريتمهاي شبکهاي گيريد* داده کاوي (Data Mrining) در زمينه هاي دسته بندي (Classification)، خوشه بندي (Clustering)، پيش بيني (Prediction)، انتخاب ويژگي (Feature Selection) و قواعدانجمني (Association Rules) با*وب سرويس و....الگوريتمlulea*سيستم هاي چندعامله ژنتيك* شبكه عصبي *هوش مصنوعي * شبيه سازي *بهينه سازي *سمينار*–الگوريتم چندهدفه* تكاملي *سيمولينک*بينايي ماشين*فازيکامينز*. Image Processing amp; Machine vision* SIMULINK, cloud storagerو IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK*و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي حاوي پايگاه داده و پروژه هاي گرافيکي تحت تمامي زبان هاي برنامه نويسي 1 - شبکه هاي عصبي مصنوعي چند لايه پرسپترون2 - شبکه هاي عصبي مصنوعي با تابع پايه شعاعي3 - درختان تصميم *****ري طبقه بندي و رگرسيوني4 - مدل هاي درختي5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني6 - سيستم هاي استنباط فازي7 - سيستم هاي استنباط فازي - عصبي8 - سيستم استنباط بيزين با استفاده از نرم افزارهاي: Clementine, SPSS, WEKA, Rapid Miner, Qnet, انجام پروژهاي برنامه نويسي دلفي ، جاوا ، ويژوال بيسيك ،وي بي دانت .وي بي 6*مطلب- پي اچ پي , ، اكسس ، سي شارپ اي اس پي *پارلوگ *پرولوگ *سي *سي پلاس پلاس *مولتيمديابيلدرو....*رديابي *مکانيابي *sar* الگوريتم تطبيقي ياد*****ري براي رتبه بندي : با رويکرد آتاماتاي ياد*****ر * شبکه هاي MANET براي کاربردهاي چند رسانه اي* ياد*****ري تقويتي براي تقسيم بار پردازشي در شبکه توزيع شده با معماري *****ريد* وسايل نقليه اي با قابليت شناسايي حملات Dos *بدافزاردرشبکه عصبي *بدافزارها وشناسايي آنها*c-means*Fuzzy k-means معماري سرويس گزا*داده گرا/*soaسيسستمهاي تشخيص نفوذ*کامپيوتري هاي بيومولکولي *سيگنال هاي الكتريكي بيو مـولـكـولي مرتب سازي شبکه Sorting-Network انجام پروژه هاي تلفن گويا ، برنامه هاي ارتباطي ، پاسخگوي خودکار ، سيستم پيغام *****ر و برنامه نويسي تحت شبکه پروژهاي شبکه حسگرو... دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت مقاله هاي جديدومعتبرباشبيه سازي *2015*2014*2013*2012*2011*2010 پروژه خودرامتخصانشان ارائه دهيدنه به موسسات انجام پروژه چون هم نمي دانند شما چه مي خواهيدوهم هزينه براي خوددريافت مي کنند درست وبا اطمينان انتخاب کنيد همراه مستندات و توضيحات کامل ، و خط به خط دستورات و نيز نحوه ساخت و چگونگي اجراي پروژه ها، بهمراه دايکيومنت (Document) تايپ شده و آماده براي صحافي بهمراه پشتيباني بعد از تحويل پروژه بعد ازتحقيق بررسي ازچند مورد تماس با ما درمورد کلاه برداري با استفاده ازاسم گروه پايتخت تحقيق وبررسي ما آغازگرديدپس ازجستجو دراينترنت متوجه شديم اشخاصي ديگري با استفاده نام اعتبارگروه نرم افزاري پايتخت اقدام به کلاه برداري و سوه استفاده ازطريق آگهي هاي همانندآگهي هاي گروه پايتخت نموده اند بدين وسيله گروه نرم افزاري پايتخت اعلام مي داردکه اين اشخاص به هيچ عنوان جزوه گروه ما نمي باشندوتنها تلفن پاسخ گو ازطريق گروه نرم افزاري پايتخت به شماره 09191022908مهندس خسروي مي باشد www.pcporoje.com 09191022908 خسروي گروه نرم افزاري پايتخت هيچ گونه مسئوليتي را جهت بي دقتي کاربران وسوه استفاده هاي احتمالي ازآنها نمي پذيرد انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,…IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عاملپيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: Database: SQLServer Access php Html Java J2EE J2me Assembly Matlab برنامه نويسيموبايل NET. تحت (Pocket PC) XML, AJAX, JavaScript) Oracle Ns2 Opnet ……, همراه :09191022908 خسروي ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين دانلودرايگان پروژه هاي دانشجويي دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب برنامه اسباب بازي فروشي*حملات سياه چالانه AODV *مقاله هاي جديد ومعتبرباشبيه سازي 2015*2014*20113*2012*2011 *ارسال مقاله وسمينار*نويز*****ري تصوير* کاربردسيستمهايچندعاملهدريادگيريالکترونيک*وب معنايي وابزارهاي ان * تشخيص چهره روي تصوير و ويديو*حذف اثر حرکت از روي تصاوير*تخمين قدرت سيگنال در شبکه مخابراتي بي سيم و تعيين مکان بهينه براي فزستنده ها *بررسي و شبيه سازي مدل سينوسي سيگنال صحبت * بررسي و مقايسه سيستمهاي عامل بلادرنگ*بررسي پروتکل SRM در شبيه ساز NS-2*بررسي روشهاي کد کردن بردارهاي جابجايي در فشرده سازي سيگنالهاي ويد يويي*طراحي و ساخت اجزاء تکرار کننده GSM*پياده سازي کدينگ کانال Reed-Solomon بر روي سيگنال ويديو بي سيم*شناسايي چهره انسان در تصاوير رن******نهان نگاري تصاوير ديجيتال در حوزه ويولت* سيستمهاي ارسال ديجيتال صوت*جداسازي سيگنالهاي صوتي مخلوط شده به روش BSS* مطالعه و بررسي امضاء هاي ديجيتال*بررسي و شبيه سازي چيدمان بهينه ادوات شبکه هاي بدون سيم*بررسي الگوريتمهاي نهان نگاري تصوير و پياده سازي آنها*سيستم اتاق عمل *بررسي روشهاي مختلف حذف نويز در سيگنالهاي ديجيتال*تحليل روشهاي فضا- زمان در سيستمهاي مخابرات بي سيم*نهان نگاري صوتي*نهان نگاري تصاوير ديجيتال با استفاده از تبديل موجک *روشهاي تکراري براي جبران اعوجاج ناشي از درونيابي *MAC جهت دار در شبکه هاي بي سيم ad hoc * Taxonomy and Survey of Cloud Computing Systemscloud *storager*محاسبات ابري opnetشبيه سازي شبکه با استفاده از WIP** روشهاي حفاظت از اطلاعات در فرآيند انتقال و دريافت مقايسه بانك هاي اطلاعاتي اسكيوال واوراكل * امنيت ATM- پايگاه داده توزيع شده سيستم مرسولات پستي اداره پست به کمک معماري سرويس گرا و تکنيک model_driven engineering شبيه سازي ns2 *تشخيص چهره انسان به روش تحليل تفکيکي خطي دو بعدي( 2D-LDA به همراه مقاله *تشخيص حرکت از طريق ورودي دوربين يا وبکم* تشخيص کارکتر و عدد در تصوير OCR* تشخيص عدد فارسي در تصوير (به همراه آموزش فارسي)* تشخيص حروف فارسي در تصوير به روش تطبيق الگو* تشخيص حروف فارسي در تصوير به روش شبکه عصبي* شبيه سازي مدولاسيون پالسهاي كدشده PCM* شبيه سازي و بررسي انواع اتصال کوتاه در ژنراتور* شبيه سازي ورقه کردن ف****** شبيه سازي بازوي ربات (به همراه مقاله)* ترميم تصوير Image *طراحي مدارهاي *ابرکامپيوترها*داده هاي با حجم بسياربالا inpainting* ترميم ويدئو Video inpainting** برنامه تشخيص بارکد (پردازش تصوير) اتحاديهخريدكارمندانوخريدكالاهايمشابهبهافراد*بررسي مکانيزم احرازهويت *fcfs*الگوريتم کاهش نويز در تصويرNoise Canceling*بررسي کليه توابع توزيع در متلبDistributions functions* پياده سازي روش گوشه شمال غربي *North-West Corner Method* برنامه تبديل اتوماتيک کد فرترن به متلب بهينه سازي تنش در تراس *پنهان‌نگاريتصاوير يا Steganography با متلب*• بدست آوردن پروفايل دما در سطح مقطع steak در زمان هاي مختلف بعد از قرار گرفتن در ظرف روغن شبيه سازي راکتور batch (ناپيوسته) و رسم نمودار غلظت ها* يكسوساز سه فاز تريستوري با *پروژه ياد*****ري ماشين يا تشخيص جنسيت زن مرد *machine learning**• تشخيص لبه تصوير توسط الگوريتم کلوني مورچه ها ACO (به همراه مقاله) پردازشتصويرWavelet بهبود مدل کاربر در وب¬سايت بصورت خودکار با استفاده ازمعناشناسي با مفاهيم خاص دامنه*پروژه هاي مهندسي معكوس *طراحي سايت b2b تشخيص هويت افراد با استفاد شناساي كف دست *نظرسنجي *الگوريتم پنتيک چندهدفه * • محاسبه جريان درون لوله و عدد رينولدز به کمک روابط سوامي و جين و دارسي-ويسباخ • شبيه سازي کنترل مقاوم عصب* تحليگرلغوي*چندضلعي *جدول متقاطع * فرستادن ايميل *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايلعنوان* امضاي ديجيتال**امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي *وب معنايي*آنتولوژي *فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدبفرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
           پايان نامه کامپيوتري کارشناسي ارشد نوشتن پروپزال و سمينار    
انجام کليه پروژه هاي دانشجوييدرسراسرايران بيش از 20 پروژه برنامه نويسيوپايان نامه پروپوزال هاي دانشجويي از دپارتمان علوم رايانه دانشگاه هاي کلمبيا هندما*****ي آلمان*سوئد*دانمارک *انگلستان *فيليپين *دبي*ترکيه و... دربانک پروژه پايتخت توسط خودگروه نرم افزاري پايتخت انجام پروژه هاي دانشجويي براي دانشجويانايراني داخل وخارجازکشوررشته کامپيوتر Several suggested student programming projects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه وپروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و....دانشگاه هاي داخل و خارج از کشوررشته کامپيوترنرم افزار*معماري کامپيوتر*هوش مصنوعي و فناوري اطلاعات و.........امنيت شبکه* مخابرات امن *تجارت الکترونيک تحت تمامي زبانها برنامه نويسي خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوقالذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبنديتوافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشيبراي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد درمورد نقش erp و سيستم هاي اطلاعاتي و ريسک در هوش تجاري بررسي انواع چالش‌هاي موجود در رايانش ابري و رايانش توري(Cloud computing amp; Grid computing) شامل مباحث امنيت (Security)، ذخيره‌سازي (Storage)، کارايي (Performance)، دسترس‌پذيري (Availability) و مديريت، تخصيص و زمانبندي منابع (Allocation and Scheduling Resources)، توازن بار(Load Balancing). بررسي انواع الگوريتم‌ها در حوزه‌ي داده‌کاوي (Data Mining)؛ طبقه‌بندي(Classification)، خوشه‌بندي(Clustering)، کشف قوانين انجمني(Association Rules)، پيش‌بيني سري‌زماني(Time Series Prediction)، انتخاب ويژ***** (Feature Selection) و استخراج ويژ***** (Feature Extraction)، کاهش بعد(Dimensionality Reduction)، شخصي سازي نتايج موتورهاي جستجو و داده‌کاوي اطلاعات آنها(Search Engine). بررسي انواع الگوريتم‌ها در حوزه‌ي شبکه‌هاي اجتماعي(Social Network)؛ کشف ساختار(structure Detection ) کشف اجتماعات(Community Detection)، تشخيص اسپم(Spam Filter). بررسي انواع تکنولوژي‌هاي ذخيره داده اي، Sql، NoSql، نگاشت کاهش (MapReduce)، هادوپ(Hadoop)، کار با Big Data. بررسي، مقايسه و بهبود انواع الگوريتم‌هاي مکاشفه‌اي، فرا مکاشفه‌اي و چند هدفه مانند الگوريتم ژنتيک(Genetic Algorithm, MOGA, NSGAII)، الگوريتم ازدحام ذرات(PSO, MOPSO)، الگوريتم مورچگان(Ant Colony)، الگوريتم زنبور عسل(Bee clolony)، الگوريتم رقابت استعماري(ICA)، الگوريتم فرهن***** (Cultural Algorithm)، الگوريتم تکامل تفاضلي(DE). بررسي انواع الگوريتم‌هاي پردازش تصوير(IMAGE PROCESSING)؛ تشخيص چهره(Face Recognation)، قطعه‌بندي تصاوير(Image Segmentation)، فشرده‌سازي تصاوير(Image Compression)، نهان‌نگاري تصاوير(Watermarking). بررسي انواع الگوريتم‌هاي ياد*****ر؛ شبکه‌هاي عصبي (ANFIS, ANN)، شبکه‌هاي بيزين(Bayesian Network)، ماشين بردار پشتيبان(SVM). استفاده از نرم‌افزار‌هاي Visual Studio، متلب(Matlab)، وکا(Weka)، رپيدماينر(Rapidminer)، Clementine، کلودسيم(Cloudsim). استفاده از زبان‌هاي Python, Java, C, C#, C++, DBMS, MySql, Sql Server, VB.NET, PHP تدوين پروپوزال، اجراي پايان نامه و طرح هاي پژوهشي و … وبررسي الگوريتمهاي شبکهاي گيريد* داده کاوي (Data Mrining) در زمينه هاي دسته بندي (Classification)، خوشه بندي (Clustering)، پيش بيني (Prediction)، انتخاب ويژگي (Feature Selection) و قواعدانجمني (Association Rules) با*وب سرويس و....الگوريتمlulea*سيستم هاي چندعامله ژنتيك* شبكه عصبي *هوش مصنوعي * شبيه سازي *بهينه سازي *سمينار*–الگوريتم چندهدفه* تكاملي *سيمولينک*بينايي ماشين*فازيکامينز*. Image Processing amp; Machine vision* SIMULINK, cloud storagerو IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK*و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي حاوي پايگاه داده و پروژه هاي گرافيکي تحت تمامي زبان هاي برنامه نويسي 1 - شبکه هاي عصبي مصنوعي چند لايه پرسپترون2 - شبکه هاي عصبي مصنوعي با تابع پايه شعاعي3 - درختان تصميم *****ري طبقه بندي و رگرسيوني4 - مدل هاي درختي5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني6 - سيستم هاي استنباط فازي7 - سيستم هاي استنباط فازي - عصبي8 - سيستم استنباط بيزين با استفاده از نرم افزارهاي: Clementine, SPSS, WEKA, Rapid Miner, Qnet, انجام پروژهاي برنامه نويسي دلفي ، جاوا ، ويژوال بيسيك ،وي بي دانت .وي بي 6*مطلب- پي اچ پي , ، اكسس ، سي شارپ اي اس پي *پارلوگ *پرولوگ *سي *سي پلاس پلاس *مولتيمديابيلدرو....*رديابي *مکانيابي *sar* الگوريتم تطبيقي ياد*****ري براي رتبه بندي : با رويکرد آتاماتاي ياد*****ر * شبکه هاي MANET براي کاربردهاي چند رسانه اي* ياد*****ري تقويتي براي تقسيم بار پردازشي در شبکه توزيع شده با معماري *****ريد* وسايل نقليه اي با قابليت شناسايي حملات Dos *بدافزاردرشبکه عصبي *بدافزارها وشناسايي آنها*c-means*Fuzzy k-means معماري سرويس گزا*داده گرا/*soaسيسستمهاي تشخيص نفوذ*کامپيوتري هاي بيومولکولي *سيگنال هاي الكتريكي بيو مـولـكـولي مرتب سازي شبکه Sorting-Network انجام پروژه هاي تلفن گويا ، برنامه هاي ارتباطي ، پاسخگوي خودکار ، سيستم پيغام *****ر و برنامه نويسي تحت شبکه پروژهاي شبکه حسگرو... دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت مقاله هاي جديدومعتبرباشبيه سازي *2015*2014*2013*2012*2011*2010 پروژه خودرامتخصانشان ارائه دهيدنه به موسسات انجام پروژه چون هم نمي دانند شما چه مي خواهيدوهم هزينه براي خوددريافت مي کنند درست وبا اطمينان انتخاب کنيد همراه مستندات و توضيحات کامل ، و خط به خط دستورات و نيز نحوه ساخت و چگونگي اجراي پروژه ها، بهمراه دايکيومنت (Document) تايپ شده و آماده براي صحافي بهمراه پشتيباني بعد از تحويل پروژه بعد ازتحقيق بررسي ازچند مورد تماس با ما درمورد کلاه برداري با استفاده ازاسم گروه پايتخت تحقيق وبررسي ما آغازگرديدپس ازجستجو دراينترنت متوجه شديم اشخاصي ديگري با استفاده نام اعتبارگروه نرم افزاري پايتخت اقدام به کلاه برداري و سوه استفاده ازطريق آگهي هاي همانندآگهي هاي گروه پايتخت نموده اند بدين وسيله گروه نرم افزاري پايتخت اعلام مي داردکه اين اشخاص به هيچ عنوان جزوه گروه ما نمي باشندوتنها تلفن پاسخ گو ازطريق گروه نرم افزاري پايتخت به شماره 09191022908مهندس خسروي مي باشد www.pcporoje.com 09191022908 خسروي گروه نرم افزاري پايتخت هيچ گونه مسئوليتي را جهت بي دقتي کاربران وسوه استفاده هاي احتمالي ازآنها نمي پذيرد انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,…IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عاملپيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: Database: SQLServer Access php Html Java J2EE J2me Assembly Matlab برنامه نويسيموبايل NET. تحت (Pocket PC) XML, AJAX, JavaScript) Oracle Ns2 Opnet ……, همراه :09191022908 خسروي ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين دانلودرايگان پروژه هاي دانشجويي دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب برنامه اسباب بازي فروشي*حملات سياه چالانه AODV *مقاله هاي جديد ومعتبرباشبيه سازي 2015*2014*20113*2012*2011 *ارسال مقاله وسمينار*نويز*****ري تصوير* کاربردسيستمهايچندعاملهدريادگيريالکترونيک*وب معنايي وابزارهاي ان * تشخيص چهره روي تصوير و ويديو*حذف اثر حرکت از روي تصاوير*تخمين قدرت سيگنال در شبکه مخابراتي بي سيم و تعيين مکان بهينه براي فزستنده ها *بررسي و شبيه سازي مدل سينوسي سيگنال صحبت * بررسي و مقايسه سيستمهاي عامل بلادرنگ*بررسي پروتکل SRM در شبيه ساز NS-2*بررسي روشهاي کد کردن بردارهاي جابجايي در فشرده سازي سيگنالهاي ويد يويي*طراحي و ساخت اجزاء تکرار کننده GSM*پياده سازي کدينگ کانال Reed-Solomon بر روي سيگنال ويديو بي سيم*شناسايي چهره انسان در تصاوير رن******نهان نگاري تصاوير ديجيتال در حوزه ويولت* سيستمهاي ارسال ديجيتال صوت*جداسازي سيگنالهاي صوتي مخلوط شده به روش BSS* مطالعه و بررسي امضاء هاي ديجيتال*بررسي و شبيه سازي چيدمان بهينه ادوات شبکه هاي بدون سيم*بررسي الگوريتمهاي نهان نگاري تصوير و پياده سازي آنها*سيستم اتاق عمل *بررسي روشهاي مختلف حذف نويز در سيگنالهاي ديجيتال*تحليل روشهاي فضا- زمان در سيستمهاي مخابرات بي سيم*نهان نگاري صوتي*نهان نگاري تصاوير ديجيتال با استفاده از تبديل موجک *روشهاي تکراري براي جبران اعوجاج ناشي از درونيابي *MAC جهت دار در شبکه هاي بي سيم ad hoc * Taxonomy and Survey of Cloud Computing Systemscloud *storager*محاسبات ابري opnetشبيه سازي شبکه با استفاده از WIP** روشهاي حفاظت از اطلاعات در فرآيند انتقال و دريافت مقايسه بانك هاي اطلاعاتي اسكيوال واوراكل * امنيت ATM- پايگاه داده توزيع شده سيستم مرسولات پستي اداره پست به کمک معماري سرويس گرا و تکنيک model_driven engineering شبيه سازي ns2 *تشخيص چهره انسان به روش تحليل تفکيکي خطي دو بعدي( 2D-LDA به همراه مقاله *تشخيص حرکت از طريق ورودي دوربين يا وبکم* تشخيص کارکتر و عدد در تصوير OCR* تشخيص عدد فارسي در تصوير (به همراه آموزش فارسي)* تشخيص حروف فارسي در تصوير به روش تطبيق الگو* تشخيص حروف فارسي در تصوير به روش شبکه عصبي* شبيه سازي مدولاسيون پالسهاي كدشده PCM* شبيه سازي و بررسي انواع اتصال کوتاه در ژنراتور* شبيه سازي ورقه کردن ف****** شبيه سازي بازوي ربات (به همراه مقاله)* ترميم تصوير Image *طراحي مدارهاي *ابرکامپيوترها*داده هاي با حجم بسياربالا inpainting* ترميم ويدئو Video inpainting** برنامه تشخيص بارکد (پردازش تصوير) اتحاديهخريدكارمندانوخريدكالاهايمشابهبهافراد*بررسي مکانيزم احرازهويت *fcfs*الگوريتم کاهش نويز در تصويرNoise Canceling*بررسي کليه توابع توزيع در متلبDistributions functions* پياده سازي روش گوشه شمال غربي *North-West Corner Method* برنامه تبديل اتوماتيک کد فرترن به متلب بهينه سازي تنش در تراس *پنهان‌نگاريتصاوير يا Steganography با متلب*• بدست آوردن پروفايل دما در سطح مقطع steak در زمان هاي مختلف بعد از قرار گرفتن در ظرف روغن شبيه سازي راکتور batch (ناپيوسته) و رسم نمودار غلظت ها* يكسوساز سه فاز تريستوري با *پروژه ياد*****ري ماشين يا تشخيص جنسيت زن مرد *machine learning**• تشخيص لبه تصوير توسط الگوريتم کلوني مورچه ها ACO (به همراه مقاله) پردازشتصويرWavelet بهبود مدل کاربر در وب¬سايت بصورت خودکار با استفاده ازمعناشناسي با مفاهيم خاص دامنه*پروژه هاي مهندسي معكوس *طراحي سايت b2b تشخيص هويت افراد با استفاد شناساي كف دست *نظرسنجي *الگوريتم پنتيک چندهدفه * • محاسبه جريان درون لوله و عدد رينولدز به کمک روابط سوامي و جين و دارسي-ويسباخ • شبيه سازي کنترل مقاوم عصب* تحليگرلغوي*چندضلعي *جدول متقاطع * فرستادن ايميل *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايلعنوان* امضاي ديجيتال**امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي *وب معنايي*آنتولوژي *فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدبفرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
           انجام پايان نامه کارشناسي ارشد درمتلب matlab    
انجام کليه پروژه هاي دانشجوييدرسراسرايران بيش از 20 پروژه برنامه نويسيوپايان نامه پروپوزال هاي دانشجويي از دپارتمان علوم رايانه دانشگاه هاي کلمبيا هندما*****ي آلمان*سوئد*دانمارک *انگلستان *فيليپين *دبي*ترکيه و... دربانک پروژه پايتخت توسط خودگروه نرم افزاري پايتخت انجام پروژه هاي دانشجويي براي دانشجويانايراني داخل وخارجازکشوررشته کامپيوتر Several suggested student programming projects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه وپروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و....دانشگاه هاي داخل و خارج از کشوررشته کامپيوترنرم افزار*معماري کامپيوتر*هوش مصنوعي و فناوري اطلاعات و.........امنيت شبکه* مخابرات امن *تجارت الکترونيک تحت تمامي زبانها برنامه نويسي خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوقالذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبنديتوافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشيبراي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد درمورد نقش erp و سيستم هاي اطلاعاتي و ريسک در هوش تجاري بررسي انواع چالش‌هاي موجود در رايانش ابري و رايانش توري(Cloud computing amp; Grid computing) شامل مباحث امنيت (Security)، ذخيره‌سازي (Storage)، کارايي (Performance)، دسترس‌پذيري (Availability) و مديريت، تخصيص و زمانبندي منابع (Allocation and Scheduling Resources)، توازن بار(Load Balancing). بررسي انواع الگوريتم‌ها در حوزه‌ي داده‌کاوي (Data Mining)؛ طبقه‌بندي(Classification)، خوشه‌بندي(Clustering)، کشف قوانين انجمني(Association Rules)، پيش‌بيني سري‌زماني(Time Series Prediction)، انتخاب ويژ***** (Feature Selection) و استخراج ويژ***** (Feature Extraction)، کاهش بعد(Dimensionality Reduction)، شخصي سازي نتايج موتورهاي جستجو و داده‌کاوي اطلاعات آنها(Search Engine). بررسي انواع الگوريتم‌ها در حوزه‌ي شبکه‌هاي اجتماعي(Social Network)؛ کشف ساختار(structure Detection ) کشف اجتماعات(Community Detection)، تشخيص اسپم(Spam Filter). بررسي انواع تکنولوژي‌هاي ذخيره داده اي، Sql، NoSql، نگاشت کاهش (MapReduce)، هادوپ(Hadoop)، کار با Big Data. بررسي، مقايسه و بهبود انواع الگوريتم‌هاي مکاشفه‌اي، فرا مکاشفه‌اي و چند هدفه مانند الگوريتم ژنتيک(Genetic Algorithm, MOGA, NSGAII)، الگوريتم ازدحام ذرات(PSO, MOPSO)، الگوريتم مورچگان(Ant Colony)، الگوريتم زنبور عسل(Bee clolony)، الگوريتم رقابت استعماري(ICA)، الگوريتم فرهن***** (Cultural Algorithm)، الگوريتم تکامل تفاضلي(DE). بررسي انواع الگوريتم‌هاي پردازش تصوير(IMAGE PROCESSING)؛ تشخيص چهره(Face Recognation)، قطعه‌بندي تصاوير(Image Segmentation)، فشرده‌سازي تصاوير(Image Compression)، نهان‌نگاري تصاوير(Watermarking). بررسي انواع الگوريتم‌هاي ياد*****ر؛ شبکه‌هاي عصبي (ANFIS, ANN)، شبکه‌هاي بيزين(Bayesian Network)، ماشين بردار پشتيبان(SVM). استفاده از نرم‌افزار‌هاي Visual Studio، متلب(Matlab)، وکا(Weka)، رپيدماينر(Rapidminer)، Clementine، کلودسيم(Cloudsim). استفاده از زبان‌هاي Python, Java, C, C#, C++, DBMS, MySql, Sql Server, VB.NET, PHP تدوين پروپوزال، اجراي پايان نامه و طرح هاي پژوهشي و … وبررسي الگوريتمهاي شبکهاي گيريد* داده کاوي (Data Mrining) در زمينه هاي دسته بندي (Classification)، خوشه بندي (Clustering)، پيش بيني (Prediction)، انتخاب ويژگي (Feature Selection) و قواعدانجمني (Association Rules) با*وب سرويس و....الگوريتمlulea*سيستم هاي چندعامله ژنتيك* شبكه عصبي *هوش مصنوعي * شبيه سازي *بهينه سازي *سمينار*–الگوريتم چندهدفه* تكاملي *سيمولينک*بينايي ماشين*فازيکامينز*. Image Processing amp; Machine vision* SIMULINK, cloud storagerو IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK*و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي حاوي پايگاه داده و پروژه هاي گرافيکي تحت تمامي زبان هاي برنامه نويسي 1 - شبکه هاي عصبي مصنوعي چند لايه پرسپترون2 - شبکه هاي عصبي مصنوعي با تابع پايه شعاعي3 - درختان تصميم *****ري طبقه بندي و رگرسيوني4 - مدل هاي درختي5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني6 - سيستم هاي استنباط فازي7 - سيستم هاي استنباط فازي - عصبي8 - سيستم استنباط بيزين با استفاده از نرم افزارهاي: Clementine, SPSS, WEKA, Rapid Miner, Qnet, انجام پروژهاي برنامه نويسي دلفي ، جاوا ، ويژوال بيسيك ،وي بي دانت .وي بي 6*مطلب- پي اچ پي , ، اكسس ، سي شارپ اي اس پي *پارلوگ *پرولوگ *سي *سي پلاس پلاس *مولتيمديابيلدرو....*رديابي *مکانيابي *sar* الگوريتم تطبيقي ياد*****ري براي رتبه بندي : با رويکرد آتاماتاي ياد*****ر * شبکه هاي MANET براي کاربردهاي چند رسانه اي* ياد*****ري تقويتي براي تقسيم بار پردازشي در شبکه توزيع شده با معماري *****ريد* وسايل نقليه اي با قابليت شناسايي حملات Dos *بدافزاردرشبکه عصبي *بدافزارها وشناسايي آنها*c-means*Fuzzy k-means معماري سرويس گزا*داده گرا/*soaسيسستمهاي تشخيص نفوذ*کامپيوتري هاي بيومولکولي *سيگنال هاي الكتريكي بيو مـولـكـولي مرتب سازي شبکه Sorting-Network انجام پروژه هاي تلفن گويا ، برنامه هاي ارتباطي ، پاسخگوي خودکار ، سيستم پيغام *****ر و برنامه نويسي تحت شبکه پروژهاي شبکه حسگرو... دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت مقاله هاي جديدومعتبرباشبيه سازي *2015*2014*2013*2012*2011*2010 پروژه خودرامتخصانشان ارائه دهيدنه به موسسات انجام پروژه چون هم نمي دانند شما چه مي خواهيدوهم هزينه براي خوددريافت مي کنند درست وبا اطمينان انتخاب کنيد همراه مستندات و توضيحات کامل ، و خط به خط دستورات و نيز نحوه ساخت و چگونگي اجراي پروژه ها، بهمراه دايکيومنت (Document) تايپ شده و آماده براي صحافي بهمراه پشتيباني بعد از تحويل پروژه بعد ازتحقيق بررسي ازچند مورد تماس با ما درمورد کلاه برداري با استفاده ازاسم گروه پايتخت تحقيق وبررسي ما آغازگرديدپس ازجستجو دراينترنت متوجه شديم اشخاصي ديگري با استفاده نام اعتبارگروه نرم افزاري پايتخت اقدام به کلاه برداري و سوه استفاده ازطريق آگهي هاي همانندآگهي هاي گروه پايتخت نموده اند بدين وسيله گروه نرم افزاري پايتخت اعلام مي داردکه اين اشخاص به هيچ عنوان جزوه گروه ما نمي باشندوتنها تلفن پاسخ گو ازطريق گروه نرم افزاري پايتخت به شماره 09191022908مهندس خسروي مي باشد www.pcporoje.com 09191022908 خسروي گروه نرم افزاري پايتخت هيچ گونه مسئوليتي را جهت بي دقتي کاربران وسوه استفاده هاي احتمالي ازآنها نمي پذيرد انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,…IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عاملپيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: Database: SQLServer Access php Html Java J2EE J2me Assembly Matlab برنامه نويسيموبايل NET. تحت (Pocket PC) XML, AJAX, JavaScript) Oracle Ns2 Opnet ……, همراه :09191022908 خسروي ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين دانلودرايگان پروژه هاي دانشجويي دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب برنامه اسباب بازي فروشي*حملات سياه چالانه AODV *مقاله هاي جديد ومعتبرباشبيه سازي 2015*2014*20113*2012*2011 *ارسال مقاله وسمينار*نويز*****ري تصوير* کاربردسيستمهايچندعاملهدريادگيريالکترونيک*وب معنايي وابزارهاي ان * تشخيص چهره روي تصوير و ويديو*حذف اثر حرکت از روي تصاوير*تخمين قدرت سيگنال در شبکه مخابراتي بي سيم و تعيين مکان بهينه براي فزستنده ها *بررسي و شبيه سازي مدل سينوسي سيگنال صحبت * بررسي و مقايسه سيستمهاي عامل بلادرنگ*بررسي پروتکل SRM در شبيه ساز NS-2*بررسي روشهاي کد کردن بردارهاي جابجايي در فشرده سازي سيگنالهاي ويد يويي*طراحي و ساخت اجزاء تکرار کننده GSM*پياده سازي کدينگ کانال Reed-Solomon بر روي سيگنال ويديو بي سيم*شناسايي چهره انسان در تصاوير رن******نهان نگاري تصاوير ديجيتال در حوزه ويولت* سيستمهاي ارسال ديجيتال صوت*جداسازي سيگنالهاي صوتي مخلوط شده به روش BSS* مطالعه و بررسي امضاء هاي ديجيتال*بررسي و شبيه سازي چيدمان بهينه ادوات شبکه هاي بدون سيم*بررسي الگوريتمهاي نهان نگاري تصوير و پياده سازي آنها*سيستم اتاق عمل *بررسي روشهاي مختلف حذف نويز در سيگنالهاي ديجيتال*تحليل روشهاي فضا- زمان در سيستمهاي مخابرات بي سيم*نهان نگاري صوتي*نهان نگاري تصاوير ديجيتال با استفاده از تبديل موجک *روشهاي تکراري براي جبران اعوجاج ناشي از درونيابي *MAC جهت دار در شبکه هاي بي سيم ad hoc * Taxonomy and Survey of Cloud Computing Systemscloud *storager*محاسبات ابري opnetشبيه سازي شبکه با استفاده از WIP** روشهاي حفاظت از اطلاعات در فرآيند انتقال و دريافت مقايسه بانك هاي اطلاعاتي اسكيوال واوراكل * امنيت ATM- پايگاه داده توزيع شده سيستم مرسولات پستي اداره پست به کمک معماري سرويس گرا و تکنيک model_driven engineering شبيه سازي ns2 *تشخيص چهره انسان به روش تحليل تفکيکي خطي دو بعدي( 2D-LDA به همراه مقاله *تشخيص حرکت از طريق ورودي دوربين يا وبکم* تشخيص کارکتر و عدد در تصوير OCR* تشخيص عدد فارسي در تصوير (به همراه آموزش فارسي)* تشخيص حروف فارسي در تصوير به روش تطبيق الگو* تشخيص حروف فارسي در تصوير به روش شبکه عصبي* شبيه سازي مدولاسيون پالسهاي كدشده PCM* شبيه سازي و بررسي انواع اتصال کوتاه در ژنراتور* شبيه سازي ورقه کردن ف****** شبيه سازي بازوي ربات (به همراه مقاله)* ترميم تصوير Image *طراحي مدارهاي *ابرکامپيوترها*داده هاي با حجم بسياربالا inpainting* ترميم ويدئو Video inpainting** برنامه تشخيص بارکد (پردازش تصوير) اتحاديهخريدكارمندانوخريدكالاهايمشابهبهافراد*بررسي مکانيزم احرازهويت *fcfs*الگوريتم کاهش نويز در تصويرNoise Canceling*بررسي کليه توابع توزيع در متلبDistributions functions* پياده سازي روش گوشه شمال غربي *North-West Corner Method* برنامه تبديل اتوماتيک کد فرترن به متلب بهينه سازي تنش در تراس *پنهان‌نگاريتصاوير يا Steganography با متلب*• بدست آوردن پروفايل دما در سطح مقطع steak در زمان هاي مختلف بعد از قرار گرفتن در ظرف روغن شبيه سازي راکتور batch (ناپيوسته) و رسم نمودار غلظت ها* يكسوساز سه فاز تريستوري با *پروژه ياد*****ري ماشين يا تشخيص جنسيت زن مرد *machine learning**• تشخيص لبه تصوير توسط الگوريتم کلوني مورچه ها ACO (به همراه مقاله) پردازشتصويرWavelet بهبود مدل کاربر در وب¬سايت بصورت خودکار با استفاده ازمعناشناسي با مفاهيم خاص دامنه*پروژه هاي مهندسي معكوس *طراحي سايت b2b تشخيص هويت افراد با استفاد شناساي كف دست *نظرسنجي *الگوريتم پنتيک چندهدفه * • محاسبه جريان درون لوله و عدد رينولدز به کمک روابط سوامي و جين و دارسي-ويسباخ • شبيه سازي کنترل مقاوم عصب* تحليگرلغوي*چندضلعي *جدول متقاطع * فرستادن ايميل *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايلعنوان* امضاي ديجيتال**امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي *وب معنايي*آنتولوژي *فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدبفرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
           انجام پروژه هاي شبکه حسگربيسيم،شبكه عصبي،سمينار،هوش    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات و.. خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد SIMULINK, cloud storager و IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK* و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : ==================================== * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,… IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عامل پيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: ==================================== VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
           انجام پروژه دانشجويي اينترنت اشياء،شبكه عصبي،هوش مصنوعي،داده    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات و.. خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد SIMULINK, cloud storager و IMAGE PROCESSING و GENETIC ALGORITHM و NEURAL NETWORK* و FUZZY LOGIC Steganalysis Facial expression Face recognition Texture segmentation Image retrieval Image segmentation Color Demosaicing ... Machine Vision: Object tracking( with all kind of methods) for various purposes Multiple Object Tracking Object Tracking with motion blur Blind motion blur deconvolution line based structure from motion Geometrical enhancemen *webrecommendation پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : ==================================== * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,… IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عامل پيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: ==================================== VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 جهت سفارش پروژه يا نياز به هرگونه اطلاع رساني فقط با ايميل زير با مادر تماس باشيد infoporoje.net@gmail.com
           انجام پروژه دانشجويي درسراسرايران MATLAB،PHP، opnet،ns3    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي بيش از 20 پروژه برنامه نويسي وپايان نامه پروپوزال هاي دانشجويي از دپارتمان علوم رايانه دانشگاه هاي کلمبيا هندما*****ي آلمان*سوئد*دانمارک *انگلستان *فيليپين *دبي*ترکيه و... دربانک پروژه پايتخت توسط خودگروه نرم افزاري پايتخت انجام پروژه هاي دانشجويي براي دانشجويان ايراني داخل و خارج ازکشوررشته کامپيوتر Several suggested student programming projaects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشور رشته کامپيوترنرم افزار*معماري کامپيوتر*هوش مصنوعي و فناوري اطلاعات و.........امنيت شبکه* مخابرات امن *تجارت الکترونيک تحت تمامي زبانها برنامه نويسي خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي حاوي پايگاه داده و پروژه هاي گرافيکي تحت تمامي زبان هاي برنامه نويسي 1 - شبکه هاي عصبي مصنوعي چند لايه پرسپترون2 - شبکه هاي عصبي مصنوعي با تابع پايه شعاعي3 - درختان تصميم *****ري طبقه بندي و رگرسيوني4 - مدل هاي درختي5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني6 - سيستم هاي استنباط فازي7 - سيستم هاي استنباط فازي - عصبي8 - سيستم استنباط بيزين با استفاده از نرم افزارهاي: Clementine, SPSS, WEKA, Rapid Miner, Qnet, انجام پروژهاي برنامه نويسي دلفي ، جاوا ، ويژوال بيسيك ،وي بي دانت .وي بي 6*مطلب- پي اچ پي , ، اكسس ، سي شارپ اي اس پي *پارلوگ *پرولوگ *سي *سي پلاس پلاس * مولتي مديا بيلدرو....*رديابي *مکانيابي *sar* الگوريتم تطبيقي ياد*****ري براي رتبه بندي : با رويکرد آتاماتاي ياد*****ر * شبکه هاي MANET براي کاربردهاي چند رسانه اي* ياد*****ري تقويتي براي تقسيم بار پردازشي در شبکه توزيع شده با معماري *****ريد* وسايل نقليه اي با قابليت شناسايي حملات Dos *بدافزاردرشبکه عصبي *بدافزارها وشناسايي آنها*c-means*Fuzzy k-means معماري سرويس گزا*داده گرا/*soaسيسستمهاي تشخيص نفوذ*کامپيوتري هاي بيومولکولي * سيگنال هاي الكتريكي بيو مـولـكـولي مرتب سازي شبکه Sorting-Network @ انجام پروژه هاي تلفن گويا ، برنامه هاي ارتباطي ، پاسخگوي خودکار ، سيستم پيغام *****ر و برنامه نويسي تحت شبکه پروژهاي شبکه حسگرو... @ دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت پروژه خودرامتخصانشان ارائه دهيدنه به موسسات انجام پروژه چون هم نمي دانند شما چه مي خواهيدوهم هزينه براي خوددريافت مي کنند درست وبا اطمينان انتخاب کنيد همراه مستندات و توضيحات کامل ، و خط به خط دستورات و نيز نحوه ساخت و چگونگي اجراي پروژه ها، بهمراه دايکيومنت (Document) تايپ شده و آماده براي صحافي بهمراه پشتيباني بعد از تحويل پروژه بعد ازتحقيق بررسي ازچند مورد تماس با ما درمورد کلاه برداري با استفاده ازاسم گروه پايتخت تحقيق وبررسي ما آغازگرديدپس ازجستجو دراينترنت متوجه شديم اشخاصي ديگري با استفاده نام اعتبارگروه نرم افزاري پايتخت اقدام به کلاه برداري و سوه استفاده ازطريق آگهي هاي همانندآگهي هاي گروه پايتخت نموده اند بدين وسيله گروه نرم افزاري پايتخت اعلام مي داردکه اين اشخاص به هيچ عنوان جزوه گروه ما نمي باشندوتنها تلفن پاسخ گو ازطريق گروه نرم افزاري پايتخت به شماره 09191022908مهندس خسروي مي باشد گروه نرم افزاري پايتخت هيچ گونه مسئوليتي را جهت بي دقتي کاربران وسوه استفاده هاي احتمالي ازآنها نمي پذيرد انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,… IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عامل پيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين @دانلودرايگان پروژه هاي دانشجويي @ دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب سيستم شهرت پيشنهاد ، تجزيه و تحليل اسپم ، ، تجزيه و تحليل اسکايپ فروشگاه مجازي تحت وب (فروشگاه اينترنتي) ، سايت آگهي ها سيستم کتابخانه ، سيستم هتل ، سيستم رستوران سيستم بيمارستان ، سيستم مطب و کلينيک ، سيستم داروخانه شبيه سازي ns2 ‏* مجموعه کامل سورس هاي ‏API‏ توابع ‏‏(2000 سورس)‏*شبيه سازي ns2 ‏* سيستم تبديل رزومه افراد به فايل html شبيه سازي سيستم عابر بانک‏ ‏* تبديل تاريخ ( شمسي به ميلادي) ‏* ساعت آنالوگ و ديجيتال (3 نوع) ‏* شبيه سازي Paint ‏* شبيه سازي بازي تنيس تمام بازي هاي هوش مصنوعي دوزو*8پازل *8وزير*هواپيما*موش وگربه و..... *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايل عنوان* امضاي ديجيتال* *امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي * وب معنايي*آنتولوژي * فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد http://tezcomputer.com www.pcporoje.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 infoporoje.net@gmail.com
           انجام پروپوزال دانشجويي كارشناسي ارشددكتري رشته كامپيوتر IT    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشوررشته کامپيوترو فناوري اطلاعات و.. خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامAي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين دانلودرايگان پروژه هاي دانشجويي دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب سيستم شهرت پيشنهاد ، تجزيه و تحليل اسپم ، ، تجزيه و تحليل اسکايپ فروشگاه مجازي تحت وب (فروشگاه اينترنتي) ، سايت آگهي ها سيستم کتابخانه ، سيستم هتل ، سيستم رستوران سيستم بيمارستان ، سيستم مطب و کلينيک ، سيستم داروخانه ‏* مجموعه کامل سورس هاي ‏API‏ توابع ‏‏(2000 سورس)‏*شبيه سازي ns2 ‏* سيستم تبديل رزومه افراد به فايل html شبيه سازي سيستم عابر بانک‏ ‏* تبديل تاريخ ( شمسي به ميلادي) ‏* ساعت آنالوگ و ديجيتال (3 نوع) ‏* شبيه سازي Paint ‏* شبيه سازي بازي تنيس تمام بازي هاي هوش مصنوعي دوزو*8پازل *8وزير*هواپيما*موش وگربه و..... *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايل عنوان* امضاي ديجيتال* *امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي * وب معنايي*آنتولوژي * فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد www.pcporoje.com http://tezcomputer.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 infoporoje.net@gmail.com
           انجام پروژه دانشجويي پايان نامه كارشناسي ارشددكتري هوش    
انجام کليه پروژه هاي دانشجويي درسراسرايران تحت تمامي زبانهاي برنامه نويسي بيش از 20 پروژه برنامه نويسي وپايان نامه پروپوزال هاي دانشجويي از دپارتمان علوم رايانه دانشگاه هاي کلمبيا هندما*****ي آلمان*سوئد*دانمارک *انگلستان *فيليپين *دبي*ترکيه و... دربانک پروژه پايتخت توسط خودگروه نرم افزاري پايتخت انجام پروژه هاي دانشجويي براي دانشجويان ايراني داخل و خارج ازکشوررشته کامپيوتر Several suggested student programming projaects for computer science majors (undergraduate, masters and PhD. students) from the Network Security Lab at Columbi@انجام پايان نامه و پروپوزال هاي دانشجويي مقاطع تحصيلي کارداني کارشناسي کارشناسي ارشددكترا و.... دانشگاه هاي داخل و خارج از کشور رشته کامپيوترنرم افزار*معماري کامپيوتر*هوش مصنوعي و فناوري اطلاعات و.........امنيت شبکه* مخابرات امن *تجارت الکترونيک تحت تمامي زبانها برنامه نويسي خدمات مشاوره اي: مشاوره رايگان انتخاب موضوع پايان نامه - انجام تمامي خدمات مربوط به تهيه پيشنهاديه پايان نامه ( proposal ) مشاوره و تدوين پايان نامه هاي مرتبط با رشته هاي فوق الذکرفناوري اطلاعات* کامپيوترو.. ارائه تمامي فصول مربوط به پايان نامه ، مطابق با جدول زمانبندي توافقي مشاوره و طراحي پرسش نامه و انجام مصاحبه و تجزيه و تحليل اطلاعات استخراجي با استفاده ازنرم افزارهاي مرتبط و در انتها ارائه مقاله اي علمي – پژوهشي براي ارائه نشريات معتبر داخلي (علمي-پژوهشي) و خارجي (ISI)IEEE*نگارش، تدوين و اديت مقاله هاي isi براي ارسال به ژورنال هاي معتبر با ايمپکت فاکتور بالا رشته فناوري اطلاعات * گرايش تجارت* الکترونيک -*کارشناسي ارشد پروژه هاي محيط سيمولينک (Simulink) پروژه هاي بازشناسي الگو (pattern recognition) پروژه هاي کدنويسي مختلف و پروژه هاي مرتبط با جعبه ابزارهاي: • Aerospace• neural network*• symbolic math*• comminucation*• bioinformatic*• curve fitting*• control system*• econometric• database*• datafeed*• filter design*• image acqusition*• signal processing*• optimization* انجام پروژه هاي حاوي پايگاه داده و پروژه هاي گرافيکي تحت تمامي زبان هاي برنامه نويسي 1 - شبکه هاي عصبي مصنوعي چند لايه پرسپترون2 - شبکه هاي عصبي مصنوعي با تابع پايه شعاعي3 - درختان تصميم *****ري طبقه بندي و رگرسيوني4 - مدل هاي درختي5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني5 - ماشين هاي بردار حامي طبقه بندي و رگرسيوني6 - سيستم هاي استنباط فازي7 - سيستم هاي استنباط فازي - عصبي8 - سيستم استنباط بيزين با استفاده از نرم افزارهاي: Clementine, SPSS, WEKA, Rapid Miner, Qnet, انجام پروژهاي برنامه نويسي دلفي ، جاوا ، ويژوال بيسيك ،وي بي دانت .وي بي 6*مطلب- پي اچ پي , ، اكسس ، سي شارپ اي اس پي *پارلوگ *پرولوگ *سي *سي پلاس پلاس * مولتي مديا بيلدرو....*رديابي *مکانيابي *sar* الگوريتم تطبيقي ياد*****ري براي رتبه بندي : با رويکرد آتاماتاي ياد*****ر * شبکه هاي MANET براي کاربردهاي چند رسانه اي* ياد*****ري تقويتي براي تقسيم بار پردازشي در شبکه توزيع شده با معماري *****ريد* وسايل نقليه اي با قابليت شناسايي حملات Dos *بدافزاردرشبکه عصبي *بدافزارها وشناسايي آنها*c-means*Fuzzy k-means معماري سرويس گزا*داده گرا/*soaسيسستمهاي تشخيص نفوذ*کامپيوتري هاي بيومولکولي * سيگنال هاي الكتريكي بيو مـولـكـولي مرتب سازي شبکه Sorting-Network @ انجام پروژه هاي تلفن گويا ، برنامه هاي ارتباطي ، پاسخگوي خودکار ، سيستم پيغام *****ر و برنامه نويسي تحت شبکه پروژهاي شبکه حسگرو... @ دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت پروژه خودرامتخصانشان ارائه دهيدنه به موسسات انجام پروژه چون هم نمي دانند شما چه مي خواهيدوهم هزينه براي خوددريافت مي کنند درست وبا اطمينان انتخاب کنيد همراه مستندات و توضيحات کامل ، و خط به خط دستورات و نيز نحوه ساخت و چگونگي اجراي پروژه ها، بهمراه دايکيومنت (Document) تايپ شده و آماده براي صحافي بهمراه پشتيباني بعد از تحويل پروژه بعد ازتحقيق بررسي ازچند مورد تماس با ما درمورد کلاه برداري با استفاده ازاسم گروه پايتخت تحقيق وبررسي ما آغازگرديدپس ازجستجو دراينترنت متوجه شديم اشخاصي ديگري با استفاده نام اعتبارگروه نرم افزاري پايتخت اقدام به کلاه برداري و سوه استفاده ازطريق آگهي هاي همانندآگهي هاي گروه پايتخت نموده اند بدين وسيله گروه نرم افزاري پايتخت اعلام مي داردکه اين اشخاص به هيچ عنوان جزوه گروه ما نمي باشندوتنها تلفن پاسخ گو ازطريق گروه نرم افزاري پايتخت به شماره 09191022908مهندس خسروي مي باشد گروه نرم افزاري پايتخت هيچ گونه مسئوليتي را جهت بي دقتي کاربران وسوه استفاده هاي احتمالي ازآنها نمي پذيرد انجام پروژه هاي برنامه نويسي دانشجوئي براي دروس دانشگاهي : * مباني کامپيوتر * برنامه سازي پيشرفته * سيستم هاي تجاري * ساختمان داده * طراحي الگوريتم * ذخيره و بازيابي اطلاعات * نظريه زبانها و ماشين ها * هوش مصنوعي * کامپايلر * ريزپردازنده,vhdl,z80,… IVR ، 8051 * شبکه هاي کامپيوتري * گرافيک کامپيوتري * مهندسي نرم افزار * پايگاه داده *كارآفريني *كارآموزي *مباحث ويژه *معماري کامپيوتر * سيستم عامل پيشرفته *ياد*****ري ماشين *پردازش موازي *روش تحقيق *سمينار *پردازش سيگنال *پردازش صوت *شبيه سازي وبهينه سازي * آزمايشگاه هاي (سيستم عامل ، ريزپردازنده ، مدار منطقي ، پايگاه داده) ليست زبانهاي برنامه نويسي تخصصي ما به شرح زير مي باشد: VB.Net2005,2008.,2010 C#.Net2005,2008.,2010 ASP.Net2005,2008.,2010 ++C C VB - Visual Basic 6.0 Pascal DELPHI Visual C++ Database: SQL Server Access php Html Java J2EE J2me Assembly Matlab برنامه نويسي موبايل NET. تحت (Pocket PC) XML, AJAX, Java Script) Oracle Ns2 Opnet ……, ليست پروژه هاي آماده تحت تمامي زبانهاي برنامه نويسي سيستم آرشيو اطلاعات پروژه هاي دانشجويي سفارش پروزه ازدانشگاه انگلستان يک نانوايي مي خواهد سيستم توزيع خودش را بهينه کند سفارش پروژه ازدانشگاه انگلستان نرم افزارارسال اس ام اس وايميل سفارش پروزه ازدانشگاه ترکيه شبيه سازي ميل سرورياهو سفارش پروزه ازدانشگاه آلمان سيستم ام ارپي سفارش پروزه ازدانشگاه هند فروشگاه اينترنتي سفارش پروزه ازدانشگاه ما*****ي کتابخانه صوتي براي لينوکس سفارش پروزه ازدانشگاه مجارستان پياده سازي همکار به همکار شبکه سفارش پروژه ازدانشگاه دبي الگوريتم fcfs سفارش پروژه ازدانشگاه فيليپين @دانلودرايگان پروژه هاي دانشجويي @ دارنده بزرگترين بانک سورس هاي آماده به تمامي زبانهاي برنامه نويسي ( انجام شده توسط خود گروه ) پايتخت درضمن برخي ازاين پروژهاهم تحت ويندوزدرآرشيوموجوداست وهم تحت وب سيستم شهرت پيشنهاد ، تجزيه و تحليل اسپم ، ، تجزيه و تحليل اسکايپ فروشگاه مجازي تحت وب (فروشگاه اينترنتي) ، سايت آگهي ها سيستم کتابخانه ، سيستم هتل ، سيستم رستوران سيستم بيمارستان ، سيستم مطب و کلينيک ، سيستم داروخانه شبيه سازي ns2 ‏* مجموعه کامل سورس هاي ‏API‏ توابع ‏‏(2000 سورس)‏*شبيه سازي ns2 ‏* سيستم تبديل رزومه افراد به فايل html شبيه سازي سيستم عابر بانک‏ ‏* تبديل تاريخ ( شمسي به ميلادي) ‏* ساعت آنالوگ و ديجيتال (3 نوع) ‏* شبيه سازي Paint ‏* شبيه سازي بازي تنيس تمام بازي هاي هوش مصنوعي دوزو*8پازل *8وزير*هواپيما*موش وگربه و..... *شبيه سازي پروتکل مسيريابي شبکه حسگر بي سيم باآپ نت پروژه هاي تشخيص هويت :عنبه *اثرانگشت *تشخيص چهره به چهره *كف دست * الگوريتم هاي خوشه بندي در شبکه هاي حسگر موبايل عنوان* امضاي ديجيتال* *امنيت اطلاعات * بررسي امنيت شبکه در مقوله پدافند غير عامل * بيومتريک (Biometric)*الگوريتم زنبورعسل *دنباله کاوي *شناسايي خط *شناسايي صورت *بينايي ماشين*هوش مصنوعي دربازي * وب معنايي*آنتولوژي * فشرده سازي تصوير*پردازش صوت * امنيت درپايگاه توزيع شده*فايل هاي ويرانگر - - - سيستم فروش و صورتحساب- سيستم حضورغياب با اثر انگشت - سيستم صندوق رستوراني و فروشگاهي با سخت افزار و نرم افزار POS گروه مهندسي پايتخت - انجام پروژه هاي دانشجويي شما با قيمتي مناسب پذيرش سفارش پروژه داخل وخارج ازکشور هرگونه کپي برداري ازآگهي غيرمجازمي باشد جهت سفارش پروژه تماس ب*****ريد ازديگرپروژهاي ماديدن فرماييد http://tezcomputer.com www.pcporoje.com http://tezcomputercom.blogfa.com مهندس خسروي 09191022908 infoporoje.net@gmail.com
          Science Project 2.0: Nibbler   

The twisted laboratory of Science Project is back with an adorable new bunny named Nibbler. This time all 41 icons are fully vector-based for extra long lasting maniacal torture themed cuteness.

This icon set contains the following 41 icons:

Baby Nibbles, Baby Nibbles - Empty Container, Baby Nibbles - Incubator - On, Baby Nibbles - Incubator - Off, Bloody Footprint, Burned Nibbler, Customize Nibbler, Customize Nibbler's Head, Ear Tagger, Nibbler, Nibbler Apps, Nibbler Candybar, Nibbler CD, Nibbler CD - Blank, Nibbler CSU - Plastic Baggie Fish, Nibbler CSU - Closed, Nibbler CSU - Open, Nibbler DVD, Nibbler DVD - Blank, Nibbler Ear Tag, Nibbler Finder, Nibbler Folder, Nibbler Folder - Blank, Nibbler Fusion, Nibbler Fusion Logo Black, Nibbler Fusion Logo Grey, Nibbler Fusion Logo White, Nibbler Neural Network - Connected, Nibbler Neural Network - Disconnected, Nibbler rarely gets a carrot, Nibbler X-Ray, Nibbler Document, Nibbler Document 02, Nibbler Document 03, NibblerDrive, NibblerDrive-Empty, NibblerDrive-Firewire, NibblerDrive-Mac, NibblerDrive-USB, NibblerDrive-Windows, Unlucky Nibbler Foot.


          A simple neural network with Python and Keras – PyImageSearch   
Learn how to create a simple neural network using the Keras neural network and deep learning library along with the Python programming language.
          Noisy Chaotic time series forecast approximated by combining Reny's entropy with Energy associated to series method: application to rainfall series   
This article proposes that the combination of smoothing approach considering the entropic information provided by Renyi's method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackey Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca - Cordoba, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi's entropy of the series. When the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors to show the predictability of noisy rainfall and chaotic time series reported in the literature.
          Vincent Granville posted a blog post   
Vincent Granville posted a blog post

          Learning Universal Computations with Spikes   
Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.
          EVOBRAIN - 90 caps   
  • Nootropic Supplement
  • Increases choline levels
  • Formulated by Sergio Espinar
  • Favors cognitive functions (memory, learning, etc.)
  • Includes Alfa-GPC, Choline and, Bacopa Monnieri and Rhodiola Rosea extracts
  • Ideal for periods of exams or mental work overload

What is EvoBrain?

EvoBrain by HSNsport is a nutritional supplement based on Alfa-GPC, Choline and extracts from Bacopa Monnieri and Rhodiola Rosea, ingredients that will act synergistically with the aim of enhancing cognitive ability and improving intellectual performance. Among the different cognitive functions we may include language, memory, coordination or executive functions that involve reasoning, judgment, planning, organization and perceptual functions.

What are the properties of EvoBrain?

The ingredients included in Evobrain have the property of being cholinergics, this means that they promote the increase of the levels of acetylcholine and choline in the brain.

Cholinergics

These compounds increase the concentration of choline in the brain, where said molecule is beneficial due to its properties that support cognitive abilities, as well as being the precursor of acetylcholine. Acetylcholine is a type of neurotransmitter (communication between neurons), located within the central nervous system and the peripheral nervous system. Within the central nervous system, cholinergic cells (neurons that use acetylcholine as a neurotransmitter) modulate and coordinate the response of neural networks in many areas of the brain, thus involved in the subsequent behavior in subjective conduct.

Within the peripheral nervous system, acetylcholine transmits signals between the motor nerves and the skeletal muscles. It works in the neuromuscular junctions and allows the motor neurons to activate the muscular action. One of the primary functions of acetylcholine is to carry signals from motor neurons to the skeletal muscles of the body.

Acetylcholine Functions

  • Plays a fundamental role in the creation of memories, verbal and logical reasoning, and the ability to concentrate.
  • May limit neurological deterioration associated with degenerative diseases
  • Improves everyday cognitive function

Alfa GPC

Alfa GPC stands for Alpha glycerylphosphorylcholine a highly bioavailable source of choline that improves brain intake of this substance more than any other source of choline. Alfa GPC is Choline in approximately 40% of its weight. Oral supplementation of Alfa GPC has a particular interest as it seems to be involved in inhibiting acetylcholinesterase, ie the activity of the cholinesterase enzyme, to prevent the destruction of the acetylcholine that has been secreted in the brain.

Alpha-GPC may be involved in reducing symptoms of cognitive decline, as this study reveals:

The amount of alpha-GPC included in this formulation can raise levels of HGH (Human Growth Hormone) in blood plasma, as reflected in this study: Kawamura T, Okubo T, Sato K, Fujita S, Goto K, Hamaoka T, Iemitsu M. Glycerophosphocholine enhances growth hormone secretion and fat oxidation in young adults. Nutrition 2012 November (11-12): 1122-6 Faculty of Sport and Health Science, Ritsumeikan University, Shiga, Japan.

This fact will contribute to the improvement and recovery at a muscular level and also referred to physical strength.

Bacopa Monnieri

EvoBrain contains extract from Bacopa Monnieri, a plant traditionally used in Ayurvedic medicine to improve memory, learning and concentration and even to improve depressive states. It is rich in alkaloids (brahmina and herpestina) saponins, beta-sitosterol and other sterols.

However, the most interesting constituents are the bacosides A and B. Bacopa has been the subject of several investigations to disclose its mechanism of action on cognitive function. Triterpenic saponins and bacosides appear to be responsible for this plant's ability to enhance neuronal activity. Bacosides seem to stimulate brain kinase activity and restore synaptic activity at the same time as helping to repair the neurons that have been damaged by free radicals, ultimately helping to improve the transmission of nervous impulses.

Bacopa Monnieri acts as an adaptogen, that is, a substance that improves our response to stress, reducing the damage it can cause on a psychic and physical level. For this purpose, it interacts with the dopaminergic and serotonergic systems and improves neuronal communication. This is done by improving the speed at which the nervous system can communicate by increasing the growth of nerve endings.

Bacopa Monnieri can reduce anxiety, depressive symptoms, memory maintenance, and psychic performance, as you can appreciate from the findings of these studies:

Rhodiola Rosea

Rhodiola Rosea is a plant that was used traditionally for its properties of fatigue reduction and also for being considered an adaptogen.

When referring to fighting fatigue, it can be related to significantly reducing the effects of prolonged physical exhaustion, in particular, with stress. This fact is of special interest in people who are not accustomed to training or exposure to such activities, or who deal with continued stress. In these studies we can observe this approximation:

Rhodiola Rosea also involves an improvement and support at a cognitive level. This is as a consequence of their physiological performance, modulating the activity of certain neurotransmitters, such as dopamine, noradrenaline and serotonin, that are implicated in various sensory responses, such as mood, appetite and overall well-being. In the following studies the conclusions of these effects can be appreciated:

Who can benefit from EvoBrain?

  • People who want to protect and nurture their cognitive functions.
  • Especially advisable for periods of great intellectual requirements.

Price:45.36 € Special Price:33.11 €
Special Expires on: Jul 2, 2017


          Neural Networks in Motor Insurance   
In the actuarial practice of motor insurance companies, estimating a fair price to charge customers for an insurance contract remains a challenging problem. The determination of the insurance premium is generally done by calculating the expected average claim for each customer profile on the basis...

Prometeia


          Detailed postmortem on making Silicon Valley’s Not Hotdog app   
bespoke neural networks for image recognition running locally on mobile
          ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models   
Abstract
Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/.

          NNAlign: a platform to construct and evaluate artificial neural network models of receptor–ligand interactions   
Abstract
Peptides are extensively used to characterize functional or (linear) structural aspects of receptor–ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and phosphatases. NNAlign is a method for the identification of such linear motifs in biological sequences. The algorithm aligns the amino acid or nucleotide sequences provided as training set, and generates a model of the sequence motif detected in the data. The webserver allows setting up cross-validation experiments to estimate the performance of the model, as well as evaluations on independent data. Many features of the training sequences can be encoded as input, and the network architecture is highly customizable. The results returned by the server include a graphical representation of the motif identified by the method, performance values and a downloadable model that can be applied to scan protein sequences for occurrence of the motif. While its performance for the characterization of peptide–MHC interactions is widely documented, we extended NNAlign to be applicable to other receptor–ligand systems as well. Version 2.0 supports alignments with insertions and deletions, encoding of receptor pseudo-sequences, and custom alphabets for the training sequences. The server is available at http://www.cbs.dtu.dk/services/NNAlign-2.0.

          Artificial Synapses Could Lead to Smarter AI   
By replicating the function of the human brain's 100 trillion synapses, scientists hope to boost the versatility of artificial neural networks.
          Peering into neural networks   
Neural networks learn to perform computational tasks by analyzing large sets of training data. But once they’ve been trained, even their designers rarely have any idea what data elements they’re processing. Image: Christine Daniloff/MIT Click for a full size image   Peering into neural networks New technique helps elucidate the inner workings of neural networks ...
          Learning to Learn without Gradient Descent by Gradient Descent   

Learning to Learn without Gradient Descentby Gradient Descent by Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas 


We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to tradeoff exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.



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          Optimal Trader 4.3.2   
Optimal Trader is a Trading Software Package for all instruments on every market! Optimal Trader is easy to use but powerful, combining technical analysis with modern signal processing and neural networks.
           Trip generation and trip distribution: comparison of neural networks and traditional methods    
Tillema, F. and Zuilekom, K.M. van and Maarseveen, M.F.A.M. van and Huisken, G. (2004) Trip generation and trip distribution: comparison of neural networks and traditional methods. In: 10th World Conference on Transportation Research, WCTR 2004, 4-8 July 2004, Istanbul, Turkey.
           Comparison of Neural Networks and Gravity Models in Trip Distribution    
Tillema, Frans and Zuilekom, Kasper M. van and Maarseveen, Martin F.A.M. van (2006) Comparison of Neural Networks and Gravity Models in Trip Distribution. Computer-Aided Civil and Infrastructure Engineering, 21 (2). pp. 104-119. ISSN 1093-9687
          AMD Launches the World’s Fastest Graphics Card for Machine Learning Development and Advanced Visualization Workloads, Radeon Vega Frontier Edition, Available Now   
AMD Launches the World’s Fastest Graphics Card for Machine Learning Development and Advanced Visualization Workloads, Radeon Vega Frontier Edition, Available Now

–  Radeon Vega Frontier Edition fuels pioneers with the power to pursue new frontiers in AI, advanced game design and photorealistic visualization –


Singapore – June 29, 2017 AMD (NYSE: AMD) today unleashed the first product based on its highly anticipated “Vega” graphics processing unit (GPU) architecture: Radeon™ Vega Frontier Edition. Radeon Vega Frontier Edition is the world’s first graphics card designed to empower the next generation of data scientists, game designers and visualization professionals, with up to 172 percent faster rendering performance than the comparable competitor card1. Through its disruptive High Bandwidth Cache Controller, the cornerstone of the world’s most advanced GPU memory architecture – HBM2 – Radeon Vega Frontier Edition expands the capacity of traditional GPU memory to 256TB, allowing users to tackle massive datasets with ease, and scored up to 33 percent faster than the competition in the DeepBench benchmark that measures the performance of basic operations involved in training deep neural networks2.

“We’re dedicating Radeon Vega Frontier Edition to all the visionaries and trailblazers who embrace new technologies to propel their industries forward to help solve mankind’s greatest problems,” said Ogi Brkic, senior director and general manager, Radeon Pro business, Radeon Technologies Group, AMD. “With this powerful solution, we’ve brought the full weight of our new ‘Vega’ GPU architecture to bear, offering unmatched3performance in the most demanding design, rendering, and machine intelligence workloads so that the world’s top creators, data scientists and game developers can reach new frontiers in their fields.”

Radeon Vega Frontier Edition Board Design

“AMD did a stunning job on the industrial design of the Radeon Vega Frontier Edition. The blue-anodized brushed aluminum shroud and lit Radeon inlays are downright elegant,” said Kelt Reeves, president of Falcon Northwest. “The high-airflow I/O bracket and vented anodized backplate are a beautifully executed example of how form can follow function and still make for a beautiful product.”

Unmatched2 Performance and TCO in Machine Learning Applications


Together with AMD’s open-source, fully scalable ROCm software platform,

Radeon Vega Frontier Edition paves the way for pioneers to continue pushing boundaries in fields like artificial intelligence (AI). Developers can now use the power of the “Vega” architecture for machine learning algorithm development on the Radeon Vega Frontier Edition faster than with any other GPU on the market2, before deploying it out to massive servers equipped with Radeon Instinct accelerators. This powerful new solution also delivers a disruptive performance per dollar equation, solidifying AMD’s leadership in compute total cost of ownership (TCO).

Advanced Photorealistic Rendering Performance

Radeon Vega Frontier Edition delivers the horsepower required for design and manufacturing firms to drive increasingly large and complex models and to deploy real-time visualization and physically-based rendering. The Radeon Vega Frontier Edition’s revolutionary memory engine also allows professionals to achieve photorealistic detail in computer-generated imagery. A visualization powerhouse, the Radeon Vega Frontier Edition GPU offers exceptional multi-GPU scaling, with 91 percent faster rendering using two Radeon Vega Frontier Edition GPUs4.

Accelerating Game Design and Immersive Workflows

The Radeon Vega Frontier Edition graphics card simplifies and accelerates game creation by providing a single GPU that is optimized for every stage of a game developer’s workflow. This includes everything from asset production to playtesting and performance optimization. With the Radeon Pro Settings user interface, users can seamlessly switch between “Radeon Pro Mode” and “Gaming Mode” to alternate between development on animation applications like Autodesk® Maya and performance optimizations with free, open source tools available through AMD’s GPUOpen initiative.

The compute power in Radeon Vega Frontier Edition and its support for an open software ecosystem also give a new breed of developers and filmmakers the ability to break new ground in virtual reality (VR) and 360-degree video content. AMD’s fastest Radeon VR Ready Creator graphics card ever, Radeon Vega Frontier Edition achieves the maximum possible score in the SteamVR benchmark, up to 21 percent higher than the multi-GPU Radeon™ Pro Duo solution5. Combined with Radeon™ Loom, AMD’s revolutionary 360-degree video stitching technology, creators can stitch high-resolution video in real time.

Radeon Vega Frontier Edition Availability

Radeon Vega Frontier Edition graphics cards are available from etailers in select regions today with an SEP of $999 USD for the air-cooled edition. The water-cooled edition is expected to launch in Q3 with an SEP of $1499.

Supporting Resources

       Learn more about Radeon Vega Frontier Edition at Pro.Radeon.com/Frontier


       Learn more about ROCm

       Become a fan of AMD on Facebook

       Follow Radeon Graphics on Twitter @Radeon


About AMD


For more than 45 years AMD has driven innovation in high-performance computing, graphics, and visualization technologies ― the building blocks for gaming, immersive platforms, and the datacenter. Hundreds of millions of consumers, leading Fortune 500 businesses, and cutting-edge scientific research facilities around the world rely on AMD technology daily to improve how they live, work, and play. AMD employees around the world are focused on building great products that push the boundaries of what is possible. For more information about how AMD is enabling today and inspiring tomorrow, visit the AMD (NASDAQ: AMD) website, blog, Facebook and Twitter pages.



Cautionary Statement

This press release contains forward-looking statements concerning Advanced Micro Devices, Inc. (AMD) including the features, functionality, availability, timing and expected benefits of Radeon™ Vega Frontier Edition products, which are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are commonly identified by words such as "would," "intends," "believes," "expects," "may," "will," "should," "seeks," "intends," "plans," "pro forma," "estimates," "anticipates," or the negative of these words and phrases, other variations of these words and phrases or comparable terminology. Investors are cautioned that the forward-looking statements in this document are based on current beliefs, assumptions and expectations, speak only as of the date of this document and involve risks and uncertainties that could cause actual results to differ materially from current expectations. Such statements are subject to certain known and unknown risks and uncertainties, many of which are difficult to predict and generally beyond AMD's control, that could cause actual results and other future events to differ materially from those expressed in, or implied or projected by, the forward-looking information and statements. Material factors that could cause actual results to differ materially from current expectations include, without limitation, the following: Intel Corporation’s dominance of the microprocessor market and its aggressive business practices may limit AMD’s ability to compete effectively; AMD has a wafer supply agreement with GF with obligations to purchase all of its microprocessor and APU product requirements, and a certain portion of its GPU product requirements, from GLOBALFOUNDRIES Inc. (GF) with limited exceptions. If GF is not able to satisfy AMD’s manufacturing requirements, its business could be adversely impacted; AMD relies on third parties to manufacture its products, and if they are unable to do so on a timely basis in sufficient quantities and using competitive technologies, AMD’s business could be materially adversely affected; failure to achieve expected manufacturing yields for AMD’s products could negatively impact its financial results; the success of AMD’s business is dependent upon its ability to introduce products on a timely basis with features and performance levels that provide value to its customers while supporting and coinciding with significant industry transitions; if AMD cannot generate sufficient revenue and operating cash flow or obtain external financing, it may face a cash shortfall and be unable to make all of its planned investments in research and development or other strategic investments; the loss of a significant customer may have a material adverse effect on AMD; AMD’s receipt of revenue from its semi-custom SoC products is dependent upon its technology being designed into third-party products and the success of those products; global economic uncertainty may adversely impact AMD’s business and operating results; the markets in which AMD’s products are sold are highly competitive; AMD may not be able to generate sufficient cash to service its debt obligations or meet its working capital requirements; AMD has a large amount of indebtedness which could adversely affect its financial position and prevent it from implementing its strategy or fulfilling its contractual obligations; the agreements governing AMD’s notes and the Secured Revolving Line of Credit impose restrictions on AMD that may adversely affect its ability to operate its business; AMD's issuance to West Coast Hitech L.P. (WCH) of warrants to purchase 75 million shares of its common stock, if and when exercised, will dilute the ownership interests of its existing stockholders, and the conversion of the 2.125% Convertible Senior Notes due 2026 may dilute the ownership interest of its existing stockholders, or may otherwise depress the price of its common stock; uncertainties involving the ordering and shipment of AMD’s products could materially adversely affect it; the demand for AMD’s products depends in part on the market conditions in the industries into which they are sold. Fluctuations in demand for AMD’s products or a market decline in any of these industries could have a material adverse effect on its results of operations; AMD’s ability to design and introduce new products in a timely manner is dependent upon third-party intellectual property; AMD depends on third-party companies for the design, manufacture and supply of motherboards, software and other computer platform components to support its business; if AMD loses Microsoft Corporation’s support for its products or other software vendors do not design and develop software to run on AMD’s products, its ability to sell its products could be materially adversely affected; and AMD’s reliance on third-party distributors and AIB partners subjects it to certain risks. Investors are urged to review in detail the risks and uncertainties in AMD's Securities and Exchange Commission filings, including but not limited to AMD's Quarterly Report on Form 10-Q for the quarter ended April 1, 2017.




1 Radeon™ Vega Frontier Edition delivers up to 172% faster performance in Maya 2017 GPGPU tests than NVIDIA GeForce Titan Xp. Testing conducted by AMD Performance Labs as of May 12th, 2017 on a test system comprising of Intel E5-1650 v3 @ 3.50 GHz, 16GB DDR4 physical memory, Windows 10 Enterprise 64-bit, Radeon™ Vega Frontier Edition / NVIDIA GeForce Titan Xp, AMD graphics driver 17.20/NVIDIA graphics driver 382.05 and Samsung 850 PRO 512G SSD.



Benchmark Application: AMD Internal Benchmark for Autodesk Maya 2017. Radeon™ Vega Frontier Edition

score: 10.38. NVIDIA GeForce Titan Xpscore: 3.81. Performance Differential: (10.38-3.81)/3.81 = ~172.44% faster performance on Radeon™ Vega Frontier Edition. PC manufacturers may vary configurations, yielding different results. Performance may vary based on use of latest drivers. RPVG-008.

2 Testing conducted by AMD Performance Labs as of May 15th 2017 with the Radeon™ Vega Frontier Edition graphics card, Intel® Xeon E5 2640v4

2.4Ghz 10C/20T, Dual Socket, 32GB per socket, 64GB Total, Ubuntu 16.04 LTS, ROCm 1.5, and OpenCL™ 1.2. The Nvidia Tesla P100, was tested on a system comprising of Intel® Xeon E5 2640v4 2.4Ghz 10C/20T, Dual Socket, 32GB per socket, 64GB Total, Ubuntu 16.04 LTS with CuDNN 5.1, Driver 375.39 and Cuda version 8.0.61. When using the DeepBench Benchmark, Radeon™ Vega Frontier Edition completed in 88.7 ms and the Nvidia

Tesla P100 completed in 133.1 ms. PC manufacturers may vary configurations, yielding different results. Performance may vary based on use of latest drivers. VG-9.


3 Testing conducted by AMD Performance Labs as of May 12th, 2017 on a test system comprising of Intel E5-1650 v3 @ 3.50 GHz, 16GB DDR4 physical memory, Windows 7 Professional 64-bit, Radeon™ RX Vega Frontier Edition / NVIDIA Geforce TitanXp, AMD graphics driver 17.20/NVIDIA graphics driver 382.05 and LITEON 512GB SSD.

4   
Benchmark Application: SPECViewperf 12.1 catia-04 viewset, Radeon™ Vega Frontier Edition score: 135.78 and NVIDIA GeForce Titan Xp score: 107.29 for ~26.55% faster performance on Radeon™ Vega Frontier Edition;
Benchmark Application: SPECViewperf 12.1 creo-01 viewset, Radeon™ Vega Frontier Edition score: 83.94 and NVIDIA GeForce Titan Xp score:
65.20 for ~28.74% faster performance on Radeon™ Vega Frontier Edition;
Benchmark Application: SPECViewperf 12.1 sw-03 viewset, Radeon™ Vega Frontier Edition score: 114.88 and NVIDIA GeForce Titan Xp score:
67.75 for  ~69.56% faster performance on Radeon™ Vega Frontier Edition.

Benchmark Application: SPECapc Siemens NX 10, Radeon™ Vega Frontier Edition score: 4.08 and NVIDIA GeForce Titan Xp score: 2.93 for ~39.25% faster performance on Radeon™ Vega Frontier Edition.

Benchmark Application: Cinebench, Radeon™ Vega Frontier Edition FPS: 183.28 and NVIDIA GeForce Titan Xp FPS: 169.72 for ~7.99% faster performance on Radeon™ Vega Frontier Edition. Scores are estimates based on AMD internal lab measurements/modelling and may vary. SPEC® and the benchmarks named SPECviewperf® and SPECapc are registered trademarks or service marks of the Standard Performance Evaluation Corporation. For more information about SPECviewperf or SPECapc, see www.spec.org. PC manufacturers may vary configurations, yielding different results. Performance may vary based on use of latest drivers. RVFE-001.


Testing conducted by AMD Performance Labs as of May 15th 2017 with the Radeon™ Vega Frontier Edition graphics card, Intel® Xeon E5 2640v4

2.4Ghz 10C/20T, Dual Socket, 32GB per socket, 64GB Total, Ubuntu 16.04 LTS, ROCm 1.5, and OpenCL™ 1.2. The Nvidia Tesla P100, was tested on a system comprising of Intel® Xeon E5 2640v4 2.4Ghz 10C/20T, Dual Socket, 32GB per socket, 64GB Total, Ubuntu 16.04 LTS with CuDNN 5.1,

Driver 375.39 and Cuda version 8.0.61. When using the DeepBench Benchmark, Radeon™ Vega Frontier Edition completed in 88.7 ms and the Nvidia Tesla P100 completed in 133.1 ms. PC manufacturers may vary configurations, yielding different results. Performance may vary based on use of latest drivers. VG-9.

4 2x Radeon™ Vega Frontier Edition is up to 91% faster rendering than 1x Radeon™ Vega Frontier Edition when using Maya with the Radeon™ ProRender plug-in. Testing conducted by AMD Performance Labs as of May 26th, 2017 on a test system comprising of Ryzen™ 7 1800X @3.60 GHz, 32GB DDR4 physical memory, Windows 10 Enterprise 64-bit, Radeon™ Vega Frontier Edition, AMD graphics driver 17.20 and Samsung 850 PRO

512GB SSD.

Benchmark Application: Maya Radeon ProRender plug-in GPU rendering option. Measurement: Render time for the Helmet scene with 8x AA,
HD720 output and 100 pass limit. 2 x Radeon™ Vega Frontier Edition render time (seconds): 135. Single Radeon™ Vega Frontier Edition render time

(seconds): 258. Performance differential: (258-135)/135 = ~91.11% faster rendering on 2 x Radeon™ Vega Frontier Edition. Scores are estimates based on AMD internal lab measurements/modelling and may vary. PC manufacturers may vary configurations, yielding different results. Performance may vary based on use of latest drivers. Performance may vary based on use of latest drivers. RPSW-002.

5 Testing conducted by AMD Performance Labs as of May 24th, 2017 on a test system comprising of Intel E5-1650 v3 @ 3.50 GHz, 16GB DDR4 physical memory, Windows 7 Professional 64-bit, Radeon™ Vega Frontier Edition/Radeon™ Pro Duo (Polaris)/ Radeon™ Pro WX 7100, AMD graphics driver 17.20 and LITEON 512GB SSD.

Benchmark Application: SteamVRPerformance Test/VRMark. Radeon™ Vega Frontier Edition SteamVRPerformance Test Score: 11. Radeon™ Pro

Duo (Polaris) SteamVRPerformance Test Score: 9.1. Radeon™ Pro WX 7100 SteamVRPerformance Test Score: 6.4. Radeon™ Vega Frontier Edition
VRMark–Orange Room Score: 8157. Radeon™ Pro Duo (Polaris) VRMark–Orange Room Score: 6596. Radeon™ Pro WX 7100 VRMark–Orange


Room Score: 6588. Scores are estimates based on AMD internal lab measurements/modelling and may vary. PC manufacturers may vary configurations, yielding different results. Performance may vary based on use of latest drivers. Performance may vary based on use of latest drivers. RPVG-009.


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          JUST THE NAMESfrom Lewis & Quark’s Neural Network Generated...   


JUST THE NAMES

a list of cat names (some more usable than others), for use in naming cats, computer servers, firstborn, etc.

  • Jeckle
  • Elbent
  • Jenderina
  • Roober
  • Snorp
  • Snox Boops
  • Cylon
  • Sookabear
  • Frere
  • Sonney Mrow
  • Jexley Pickle
  • Marper
  • Foppin
  • Toby Booch
  • Snowpie
  • Big Wiggy Bool
  • Macha Boo
  • Mr Whinkles
  • Timble
  • Macfallon
  • Machaka
  • Licky Cat
  • Mr Bincheh
  • Macnaw
  • Maxy Fay
  • Tim Hike
  • Mr Gruffles
  • Grips
  • Liony Oli
  • Lingo
  • Lingley
  • Conkie
  • Lasley Goo
  • Mr Took
  • Linky
  • Marvish
  • Mag Jeggles
  • Corko
  • Maggin
  • Mcguntton
  • Mara Tatters
  • Mr Tiggie
  • Mr. Skuffles
  • Mr. Hinkles
  • Mush Jam
  • Tilly-Mapper
  • Mr. Jubble
  • Mumcake
  • Muppin
  • Mr O

Backup choices:

  • Cutzerinda
  • Galorub
  • Pans
  • Sofa
  • Shotkie
  • Ouiho
  • Pope
  • Kogon
  • Ro Larky
  • Rorka Bot
  • leperaONtiea
  • Malool
  • Scagkaleoru
  • Clagmlh
  • Mice
  • iiia
  • Ha LuoleryPlogalasnfalon
  • Hubla Ssrerosti
  • Negflun Mery
  • Booii
  • Balllucidoux

Definitely backup choices:

  • Trickles
  • Poot
  • Moosh Papper
  • Clotter
  • Moan
  • Toot
  • Cloobie
  • Slarkbir
  • Jenky
  • Pissy
  • Schitty
  • Retchion
  • Pappy
  • Dopia
  • Pilly
  • Scabbys
  • Pish
  • Mesladewench
  • Souffungy
  • Mr Tinkles

          Facundo Batista: Europython 2016, la conferencia   


El evento estuvo dividido principalmente en tres partes, tutoriales para principiantes, conferencia propiamente dicha, y los sprints.

A los tutoriales (Beginners day, y Django girls), que duraban un día, no fui.  La conferencia fue de lunes a viernes. Y los sprints fueron sábado y domingo.  Del primer día de conferencia ya les conté, y el domingo estuve viajando. El resto, se los cuento acá :)


Charlas interesantes

Recopilación de lo que más me gustó de la conferencia... ojo, en algunos casos incluyo links a los videos o presentaciones mismas, en otros no porque me dió paja buscarla, pero tienen que estar :)

Por lejos, la mejor Keynote fue la de Jameson Rollins, "LIGO: The Dawn of Gravitational Wave Astronomy", aunque también estuvo buena la de Naomi Ceder, "Come for the Language, Stay for the Community". Tercera podríamos poner "Scientist meets web dev: how Python became the language of data", por Gaël Varoquaux. El resto me aburrió un poco, o no me interesó tanto.

LIGO is a...

Otras charlas que me gustaron fueron "High Performance Networking in Python" de Yury Selivanov, "Build your first OpenStack application with OpenStack PythonSDK" por Victoria Martinez de la Cruz, "Implementación de un Identificador de Sonido en Python" por Cameron Macleod, "FAT Python: a new static optimizer for Python 3.6" de Victor Stinner, "CFFI: calling C from Python" de Armin Rigo, "The Gilectomy" de Larry Hastings, "A Gentle Introduction to Neural Networks (with Python)" de Tariq Rashid, y "Music transcription with Python" de Anna Wszeborowska.

De esta última charla me quedé con el proyecto a futuro (ya lo anoté, está en la posición 1783461° entre otros proyectos) de mostrar en tiempo real, usando Bokeh, la info que levanta y las transformaciones que va haciendo.

Imagen típica de Bilbao

También quiero resaltar dos lightning talks: a Armin Rigo mostrando un "Reverse debugging for Python", y una de alguien que no me acuerdo mostrando "A better Python REPL".


Mis presentaciones

Ya les hablé de la charla que había dado el lunes, pero aprovecho y les dejo el video de la misma.

El martes dí Entendiendo Unicode, en castellano. Fue la 12° vez que la doy, y me podrán decir "dejá de robar con la misma charla"... qué quieren que les diga, el público se renueva. Yo también a veces pienso si no será demasiado, ¡pero a la gente le gusta y le sirve! Una decena de personas me saludaron y me comentaron lo buena y lo útil que fue la charla. Así que nada, la seguiré ofreciendo en próximas conferencias, :). El video, acá.

Espacio común de trabajo

Además de esas dos presentaciones "largas", dí dos lightning talks. La primera sobre fades; no es la primera vez que la doy, pero la había renovado y traducido al inglés, y estuvo muy bien. La segunda fue sobre Python Argentina. La hice el mismo viernes, a los apurones, pero a la gente le gustó mucho (me sorprendió la cantidad de veces que se rieron en esos cinco minutos (cinco minutos que tuve que pelear, como ven en el video, porque me querían dar dos, luego la confusión de que yo iba a hablar de una PyCon).


Cierre

El sábado, estuve sprinteando, trabajando con fades, más que nada ofreciendo ayuda a gente que quería usarlo o que querían enterarse más sobre el proyecto. Incluso se acercó alguien con un detalle, lo charlamos, lo solucionamos y hasta hice un pull request.

Pintxo

Ese sábado era mi última noche en Bilbao. Medio coordinamos con Juan Luis y fuimos a cenar pinchos con otras personas, luego por una cerveza. Y cuando estaba cerrando la noche, tipo once y media, me comentaron de una zona en la ciudad donde hay toda una movida heavy y punk.

No me la podía perder.

Así que nos fuimos cinco personas hasta allí, saltamos por tres o cuatro bares, tomando algo en cada uno, escuchando muy buena música, terminando en un antro de mala muerte, jugando metegol, pasando música que elegíamos nosotros, y disfrutando mucho.

En un bar punkie

A eso de las dos y media dí por concluido el paseo, porque a las cuatro me pasaba a buscar el taxi, así que con Oriol (uno de los chicos) nos tomamos un taxi, llegué a la habitación, terminé de armar todo, me pegué una ducha, dejé las llaves en la mesa de la cocina y arranqué las 23 horas de viaje que me iban a reecontrar con mi familia :)

Todas las fotos de la conferencia y Bilbao, acá.


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